Molinero Guerra

Agustín Molinero Guerra

Nadia Mokni
email: nadia.mokni@irsn.fr

Yu-Jun Cui

Pierre Delage

Anh Minh Tang

Patrick Aimedieu

Frédéric Bernier

Michel Bornert

Impact of initial structural heterogeneity on long term swelling behavior of MX80 bentonite pellet/powder mixtures

Keywords: heterogenous strcutural distribution, Microfocus X-ray Computed Tomography, Sealex in situ tests, MX80 bentonite pellet powder mixture heterogenous structural distribution, Microfocus X-ray Computed Tomography, Sealex in situ tests, MX80 bentonite pellet powder mixture https://mc06.manuscriptcentral.com/cgj-pubs

To better understand SEALEX in situ tests result´s carried out at Tournemire Underground Research Laboratory , the hydro-mechanical behavior of a pellet/powder MX80 bentonite mixtures prepared at a dry density of 1.49 Mg/m 3 were investigated by means of µ-CT observations and laboratory small scale infiltration tests. Radial and axial swelling pressures as well as relative humidity were monitored while wetting. Two configurations were considered: for the first, a pellet/powder mixture was prepared following a specific protocol to minimise initial structural heterogeneity; the second one was specially designed to study a strong heterogeneous mixture distribution. µ-CT observations performed on the two samples during hydration revealed an apparently homogeneous sample for the first mixture after 100 days of hydration. For the second specimen, several voids were still observed after 40 days of hydration. A comparison was made between the in situ and mock-up tests. It was observed that the evolutions of radial and axial swelling pressures depend on the initial heterogeneous distribution of the mixture. This heterogeneity is due to the different dry density values at the vicinity of the different sensors. The final values of axial swelling pressures were different for both configurations for the same global dry density.

Introduction

Bentonite high density pellet and powder mixture is being evaluated as possible sealing materials in deep geological repositories. In spite of the operational advantages related to the use of the mixture (e.g. ease of handling and minimisation of technological gaps), structural heterogeneities resulting from the installation process constitutes a matter of concern and require special approaches to adequately describe the material behaviour during hydration. In D r a f t 3 this context, the Institute of Radiation protection and Nuclear Safety (IRSN) has launched SEALEX project to investigate the long-term hydraulic performance of sealing systems in normal and critical scenarios, as well as different core compositions. A series of in-situ experiments have been performed in IRSN's Underground Research Laboratory (URL -Tournemire, France) [START_REF] Mokni | Hydro-mechanical analysis of SEALEX in situ tests-Impact of technological gaps on long term performance of repository seals[END_REF][START_REF] Mokni | Analysis of hydro-mechanical behavior of compacted bentonite/sand mixture using a double structure formulation[END_REF]. This work focuses on the last sealex test in which a mixture of pellets and powder of bentonite with a proportion of 80/20 in dry mass was investigated. Based on the design of the in-situ experiments, two laboratory mock-up tests (1/10 th scale) were performed aiming at studying the hydro-mechanical behavior and the structure changes of the material during hydration.

The hydro-mechanical behavior of different configurations of sealing plugs has been investigated at both laboratory and field scales [START_REF] Mokni | Hydro-mechanical analysis of SEALEX in situ tests-Impact of technological gaps on long term performance of repository seals[END_REF][START_REF] Mokni | Analysis of hydro-mechanical behavior of compacted bentonite/sand mixture using a double structure formulation[END_REF][START_REF] Wang | Experimental study on the swelling behavior of bentonite/claystone mixture[END_REF][START_REF] Wang | A comparative study on the hydro-mechanical behavior of compacted bentonite/sand plug based on laboratory and field infiltration tests[END_REF]Saba et al., 2014). The relationship between swelling pressure and bentonite dry density of the material is an important result from these investigations [START_REF] Börgesson | Modelling of the physical behavior of clay barriers close to water saturation[END_REF][START_REF] Dixon | Swelling and hydraulic properties of bentonites from Japan, Canada and the USA[END_REF][START_REF] Lloret | Mechanical behavior of heavily compacted bentonite under high suction changes[END_REF][START_REF] Imbert | Hydro-mechanical response of a bentonite pellets/powder mixture upon infiltration[END_REF][START_REF] Karnland | Sealing ability of Wyoming bentonite pellets foreseen as buffer material -laboratory results[END_REF][START_REF] Gens | Hydro-mechanical Behavior of a Heterogenous Compacted Soil: Experimental Observations and Modelling[END_REF][START_REF] Villar | Basal spacings of smectite in compacted bentonite[END_REF][START_REF] Wang | A comparative study on the hydro-mechanical behavior of compacted bentonite/sand plug based on laboratory and field infiltration tests[END_REF]Saba et al., 2014;[START_REF] Schanz | Swelling pressure characteristics of compacted Chinese Gaomiaozi bentonite GMZ01[END_REF]. However, this relationship was mainly established based on the axial swelling pressure of the bentonite-based materials, and a few results of radial swelling pressure were considered for this purpose. Additionally, very few experiments focused on the evolution of dry density heterogeneity upon hydration and its influence on the swelling behavior. Furthermore, few investigations have been conducted on the hydromechanical behavior of bentonite pellet/powder mixture which is one of candidate sealing materials [START_REF] Imbert | Hydro-mechanical response of a bentonite pellets/powder mixture upon infiltration[END_REF]Van Geet et al. 2005;[START_REF] Molinero-Guerra | In-depth characterisation of a mixture composed of powder/pellets MX80 bentonite[END_REF]. This material, consisting of a mixture of low-density bentonite powder and highly compacted bentonite pellets, is obviously highly heterogeneous in its initial state. [START_REF] Molinero-Guerra | In-depth characterisation of a mixture composed of powder/pellets MX80 bentonite[END_REF] demonstrated that preparing samples of a mixture of MX 80 bentonite pellet/powder at the D r a f t same target dry density with the same fabrication protocol does not ensure an initial homogenous structural distribution. The degree and distribution of heterogeneity might vary during hydration; thus, the average dry density might be not sufficient to characterize its final state. Another important characteristic of the bentonite mixture is the multimodal nature of its porous network which governs its overall hydromechanical properties.

The aim of this work is to investigate the effect of initial heterogeneous structural distributions on the swelling capacity and microstructural evolution of the bentonite pellet/powder mixture upon hydration. To account for possible structural heterogeneities resulting from the installation process in real disposal repositories, two configurations were considered. In the first configuration, the specimen consists of a mixture of MX80 bentonite pellet and powder with a proportion of 80/20 in dry mass, similarly to the SEALEX in-situ test. Special effort has been made to minimize initial structural heterogeneities. In the second configuration, a highly heterogeneous specimen, with half consisting of MX80 bentonite pellet and half of MX80 bentonite pellet/powder mixture was tested. The results of the small scale tests are presented along with the results of in situ SEALEX test. Comparison of the small scale laboratory tests and the in-situ experiments provides useful information regarding the test scale effect.

Materials

The studied soil is a mixture of MX80 bentonite pellet and powder (80/20 in dry mass). The bentonite investigated comes from Wyoming, USA. It was provided by the Laviosa-MPC company under the commercial name Expangel SP7 for pellets and SP30 for the powder. The MX80 bentonite has a smectite content of 80% with some inclusions of crystals (quartz, calcite and pyrite). The cation exchange capacity (CEC) is 98 meq/100g, with Na+ as major D r a f t exchangeable cation (52 meq/100g, with also 1.2 meq/100g for K + , 10 meq/100g for Mg 2+ and 37 meq/100g for Ca 2+ ) . The liquid limit is 560%, the plastic limit is 62% and the unit mass is 2.77 Mg/m 3 (Saba et al. 2014).

Pellets of bentonite were produced by Laviosa-MPC company by compacting a powder of MX80 bentonite in a mould of 7 mm in diameter and 7 mm in height. Compaction was performed at a water content of 6±1% by applying instantaneously the compaction effort, resulting in a pellet dry density ρ d = 2.06±0.06 Mg/m 3 , corresponding to a void ratio e = 0.30±0.07. The pellets were stored in the laboratory in a hermetic plastic box at 20°C. The initial suction (s = 135 ± 3 MPa) was measured in the laboratory with a chilled mirror dew point tensiometer (Decagon WP4C), corresponding to an initial water content w = 7.25%, slightly higher than the fabrication water content value (6±1%), due to further hydration after fabrication.

The MX80 bentonite powder was produced by crushing pellets. An initial water content of 3.17% was found in the laboratory after drying at 105°C for 24h, corresponding to an initial suction s = 190.9 MPa (measured with a chilled mirror dew point tensiometer -Decagon WP4).

Methods

In order to investigate the HM behavior of the mixture an in situ test and four mock up infiltration tests have been performed. At small scale, two kind of experimental cells have been designed: two stainless steel cells equipped with relative humidity and swelling pressure sensors to investigate the macrostructural hydro-mechanical behaviour of the material and two Polymethyl methacrylate cells to investigate microstructural changes of the mixture while wetting by means of Microfocus X-ray Computed Tomography (µ-CT) observations. µ-CT is a non desctructive 3D imaging technique increasingly used to investigate various materials D r a f t 6 intended as geological and engineered barriers in deep geological repositories like Boom and Opalinus clays (e.g. Chen et al 2014; [START_REF] Van Geet | Self-sealing capacity of argillaceous rocks: Review of laboratory results obtained from the SELFRAC project[END_REF], compacted bentonite (e.g. [START_REF] Kozaki | Observation of Microstructures of Compacted Bentonite by Microfocus X-Ray Computerized Tomography (Micro-CT)[END_REF]Suuronene et al., 2014), and compacted bentonite mixtures (Van Geet et al, 2005;[START_REF] Kawaragi | Microstructure of saturated bentonite characterized by X-ray CT observations[END_REF]Saba et al., 2014).

SEALEX in situ test

The last SEALEX in-situ performance test namely PT-N4 has been performed, aiming at investigating the long term homogenisation of seals composed of the mixture of MX80 bentonite pellets and powder. When installing the experiment, a horizontal borehole (0 ± 2°), with a 60 cm diameter and 540 cm long was prepared by excavation in the Tournemire URL operated by IRSN, located in a Mesozoic sedimentary basin on the western edge of the French Causses ( Mokni et al., 2016). The bentonite-based core with a total length of 120 cm consists of a granular mixture of bentonite pellets and powder in a ratio of 80/20 (in dry mass). The bentonite core was constructed using an auger conveyor; vibration was applied through the auger itself in order to achieve the target dry density of 1.49 Mg/m 3 .

The core was confined between two fixed stainless steel lids, namely upstream and downstream lids, which provided both hydraulic and mechanical closures (Figure 1). The lids consist of a stainless steel circular plate and a cylindrical stainless steel tube welded all around its periphery. It has a series of three rubber inflatable cushions (O-ring) all around and passing for the hydration tubings. The hydration at both sides of the lids (upstream and downstream) is based on special geotextile mats fed with water distribution tubes. The reduction in thickness of the mats due to the swelling of the buffer is limited to 4 MPa. The hydration system is equipped with a water distribution panel fed by a weighed stainless steel water tank connected with inflow lines to both hydration surfaces (downstream and upstream). The D r a f t confining system consists of a support tube inserted into the cylindrical tube of the downstream lid up to rest against the circular plate and of a closure plate (1400 mm in diameter) placed at the outer face of the borehole and secured by four bolts anchored to the rock.

In order to avoid potential flow paths along cables and sensors, and thereby, to limit the disturbance of the clay core as much as possible, wireless sensors were used. Three types of sensors were installed within the seal: 5 total pressure cells (Kulite BG-1-0234-6 MPa), 8 pore pressures sensors (Kulite ETM-200-375-1 MPa) and 8 relative humidity sensors (commercially-available Sensirion SHT75) (Figure 2). Three total pressure sensors were installed on the surface of the core at 60 cm from the downstream saturation face in order to measure the radial swelling pressure. Two total pressure sensors were installed at 0 and 120 cm from the downstream saturation face to measure axial swelling pressure. Hydration was performed by injecting synthetic water with the same chemical composition as the pore water of the Callovo-Oxfordian claystone from the ANDRA underground research laboratory in Bure (Table 1). Injection was carried out using a counter pressure of 0.2 MPa during few hours, and then the backward pressure was released.

Experimental mock-up tests

Mock-up tests to investigate microstructural evolution of the mixture

The evolution of the microstructural distribution of the material upon hydration was investigated by means of Microfocus X-ray Computed Tomography observations (µ-CT). A special set-up consisting of a transparent PMMA (Polymethyl methacrylate) cell was designed (Figure 4). It has 60 mm of inner diameter and 120 mm of height, which corresponds to 1/10 th scale of the SEALEX in situ tests. The mixture is prepared inside the cell, between two pore stones and filter papers. The rigid PMMA cell (30 mm thick) and the blocked piston ensure a D r a f t constant-volume hydration condition. The mixture is saturated from both top and bottom sides, as in the SEALEX in situ experiments. More details on the experimental cell could be found in [START_REF] Molinero-Guerra | Analysis of the structural changes of a pellet/powder bentonite mixture upon wetting by X-ray computed microtomography[END_REF]. µ-CT scans were carried out on both mixtures at initial state and during hydration by using an "Ultratom" microtomograph (RX Solutions, France). Images were reconstructed using the software Xact (RX Solutions). The source is a microfocus X-ray tube Hamamatsu L10801 and the imager is a Paxscan Varian 2520V (1960 x 1536 square pixels 127 µm size).

The X-ray source parameters were 160 kV and 120µA; the voxel size was 50 µm. The samples were scanned using 5760 projections in helical mode. After reconstruction, 2800 horizontal slices were obtained (16 bit images, 1499 x 1499 pixels). An external metal filter consisting of a 1.5mm thickness cooper plate was used in the µ-CT source. This filter allows reducing the low energy photon component of the X-ray beam, reducing the beam hardening effect.

Mock-up tests to Investigate hydro-mechanical behavior of the mixture

Two identical small-scale infiltration cells (mock-up tests, Figure 3) were designed in order to investigate the hydro-mechanical behavior of the mixture. The dimensions correspond to 1/10 of in situ SEALEX experiments (60 mm in diameter and 120 mm in height). The confined saturation conditions for the pellet/powder bentonite mixture are ensured by a rigid structure and a piston blocked by a screw. The material was saturated from both sides (top and bottom) as in the SEALEX in situ experiment. Six total pressure sensors were installed in the cell (SP20, SP40, SP60, SP80 and SP100), which allowed the measurement of the radial swelling pressures at different positions (h = 20, 40, 60, 80, and 100 mm from the bottom side). The accuracy of the sensors is ±1%. A force transducer was installed under the cell base, monitoring the axial swelling pressure. The relative humidity was also recorded by using five D r a f t relative humidity sensors (RH31, RH51, RH71, RH91 and RH111) placed at different heights in the cell (h = 31, 51, 71, 91 and 111 mm, see Figure 3).

Sample preparation and adopted protocol

To account for possible structural heterogeneities resulting from the installation process in real disposal conditions, two configurations were considered at the same global dry density of 1.49 Mg/m 3 . In the first configuration, the specimen (samples 1 and 1a) consists of a mixture of MX80 bentonite pellet and powder with a proportion of 80/20 in dry mass, similarly to SEALEX in-situ test. A special preparation protocol was adopted to minimize initial structural heterogeneities in terms of particle arrangement [START_REF] Molinero-Guerra | In-depth characterisation of a mixture composed of powder/pellets MX80 bentonite[END_REF]. It consists in filling the cell by packets corresponding to one layer of pellets spread over the base of the cylinder and by adding the corresponding amount of powder (taking into account the proportion 80% pellets -20% powder in dry mass) (Figure 5a). In the second configuration, a highly heterogeneous specimen (samples 2 and 2a) consisting of a mixture of MX80 bentonite pellet and powder prepared at different proportions was tested (Figure 5b). In this case, the half bottom part of the cell was filled with a 66.7% pellet/33.3% powder mixture (proportions in dry mass) -corresponding to a dry density of 1.79 Mg/m 3 , to ensure that all inter-pellet voids are filled with powder. On the contrary, the top half of the cell was filled with only pellets of bentonite at a dry density of 1.19 Mg/m 3 . The latter case simulates possible defects that might results from in situ installation process (e.g segregation, existence of zones with an assembly of pellets with no powder filling the voids or zones with an accumulation of powder)

All the tests started by opening the water inlet valves. At the beginning, air in the base or in the piston was evacuated by opening the air outlet valve until no air bubble was observed in D r a f t the pipes. Water was then injected through both the top and the bottom of the sample. Values of radial and axial swelling pressure as well as relative humidity were recorded automatically by a data logger. The volume of injected water was also monitored during hydration.

Mock up tests experimental results

Microstructural observations

The microstructural evolution during hydration was investigated by µ-CT observations on two MX 80 bentonite pellet and powder samples (named sample 1a and sample 2a). Sample 1a was prepared to minimise initial heterogeneities and sample 2a was strongly heterogeneous.

Figure 6 shows the vertical µ-CT sections of sample 1a at different times. The initial state shows the presence of larger voids between the pellet and the porous stone at the top of the mixture, most probably due to segregation during the sample fabrication. Note also the presence of large inter-pellet voids which are not filled with powder, particularly in the peripheral part of the specimen. After 2 days of hydration, the top large inter-pellets voids are completely sealed due to the vicinity to the hydration front. During hydration, the polydisperse assembly of the highly compacted pellets and powder is progressively lost. Interpellet voids are still observed after 56 days of hydration, even though the pellets located at the furthest position from both hydration fronts have already swollen at this time. An apparently global homogeneous sample is observed after 100 days of hydration at the considered resolution (50 µm/voxel). At this time, almost all the voids are completely sealed.

Figure 7 shows a zoom of an assembly of pellets located at the middle of sample 1a. These observations allow better understanding the pellets structural changes during hydration. After 11 days of hydration, liquid water has not reached this section but hydration is ensured by vapor transfer. Several cracks can be observed within the pellets. These cracks will play the role of preferential paths for vapor transfer during saturation. After 27 days, new cracks are D r a f t 11 observed and the pellets are progressively degraded. The initial granular structure is almost lost after 56 days but some inter-pellet voids are still observed.

Figure 8 shows the vertical µ-CT sections of the highly heterogeneous sample 2a during wetting. A clear distinction can be observed between the top and bottom halves of the sample.

For the 100-pellt/0-powder mixture (situated between 60 mm and 120 mm from the bottom hydration front), the initial granular structure completely disappears after 1 day of hydration.

Pellets shape can be hardly distinguished; but several large voids can still be observed. After 40 days, the voids are almost sealed by swollen bentonite. A view of the structural evolution of the mixture at a horizontal section located within the looser upper part of the sample at 110 mm from the bottom hydration front is shown in Figure 9. Initially, an assembly of pellets with larger inter-pellet voids not filled with powder can be observed. After 30 min of hydration, the shape of pellets is completely lost as they swell instantaneously. After 90 min of hydration, all voids are completely sealed, resulting in an apparently homogeneous bentonite mixture. More details on image analysis of µ-CT observation of sample 1a could be found in [START_REF] Molinero-Guerra | Analysis of the structural changes of a pellet/powder bentonite mixture upon wetting by X-ray computed microtomography[END_REF] For the denser lower part of sample 2a ( 66.7-pellet/33.3-powder), only pellets in contact with the bottom porous stone start swelling after 1 day of hydration, but no significant changes are observed in the rest of the sample (Figure 14). Unlike the looser upper part of the sample where the material was rapidly inundated with water, for the denser lower part it is suspected that saturation occurs by advection of liquid water and diffusion of vapor. After 5 days, a homogenous saturated bentonite layers is formed at the bottom boundary of the sample. At this layer, large macro voids are invaded by the swollen bentonite and the hydraulic conductivity becomes very low, favoring the saturation of the remaining unsaturated zones by vapor transfer. Interestingly, the material located at the limit between the denser and looser part (at 60 mm from the bottom) starts swelling rapidly after 5 days since it D r a f t 12 is also hydrated by water coming from the looser part. Significant changes are observed after 35 days. In the looser part, most inter-pellets voids are already sealed, while in the denser part an increase of the thickness of the outer apparently homogenous bentonite layer is observed.

However, after 40 days, several inter-pellets voids are still identified in the looser part; but they are progressively sealed, as it is observed within the outer layer closer to the hydration front (Figure 15). Simultaneously, at the denser part of the sample, the pellet shape can be still noticeable. Unfortunately, the µ-CT observations were stopped at 40 days.

Mechanical results

Figure 10 displays the swelling pressure evolution of the two investigated samples (sample 1 and 2) after 300 days of hydration. Two distinct hydro-mechanical responses can be identified for both mixtures. For sample 1 (Figure 10a), the pattern of behavior is very similar in all positions ( SP 20,40,60,and 80). The swelling pressure increases at different rates depending on the distance to the hydration front, then reaches a nearly stationary value after 200 days.

Nevertheless, a negative rate is observed at the beginning of the hydration process at SP40. This negative rate can be related to a rearrangement of the material structural distribution at this zone at the beginning of the hydration process. After 300 days, the highest radial swelling pressure (4.6 MPa) is measured at SP80 located at 40 cm from the top hydration face. At SP60 the magnitude of the swelling pressure is lower and reaches 3.8 MPa after 300 days. At SP100 located at 20 mm from the top hydration front a different behaviour is observed. The swelling pressure increases very rapidly and reaches a peak of about 2.1 MPa (a zoom is presented in Figure 11). After about 5 hours a significant decrease of the swelling pressure occurs, to a minimum value of 1.5 MPa. The swelling pressure increases again at slower rate and reaches a value of 2.55 MPa after 60 days. Afterwards, the swelling pressure increases and is stabilized at 3.3 MPa after 200 days. The peak occurrence followed by a decrease corresponds to the reorganisation of the microstructure characterized by the collapse of the D r a f t 13 macrospores between the bentonite grains. It is worth noting that the horizontal swelling pressure measurements are strongly influenced by the local microstructural distribution at the vicinity of the sensors. The non-peak occurrence at SP40, SP60, and SP80 suggest that the sensors were placed in zones where high density pellets are tightly arranged with few or no grains of bentonite powder in between of them. Similar trends were observed for the axial swelling pressure. However, in this case no macrostructural collapse is observed, indicating that collapse in some zones is compensated by the non-collapsing behavior of other zones.

A different pattern of behavior is observed for the highly heterogeneous mixture (sample 2, Figure 10b). At SP20 located within the denser bottom part of the mixture, a fast increase of the swelling pressure is observed at the beginning of the test, reaching a peak value of 6.1

MPa. Then, it decreases until a value of 3.6 MPa after 75 days of hydration. Swelling pressure increases slightly between 150 and 300 days of hydration, being 3.8 MPa the highest value.

On the contrary, at SP40 the swelling pressure increases gradually, and then reaches a nearly stationary value after 100 days. At SP60 located at the limit between the denser and the looser part of sample 2, a fast increase of swelling pressure is recorded, with a rate higher than that at SP40, until reaching a peak at 1.6 MPa. Then, the swelling pressure decreases slowly to a value of 1.2 MPa. After 72 days, an increase is observed again with the same rate as at SP40. After 300 days of hydration, a swelling pressure of 2 MPa is measured.

The lowest swelling pressure increase rate and magnitude are observed at SP80 and SP100 respectively, located at the upper looser part of sample 2 (1.19 Mg/m 3 ), at 40 and 20 cm form the upper hydration front. At SP80, the radial swelling pressure increases very slowly until reaching a value of 0.3 MPa after 75 days of hydration. Then, an increase of swelling pressure is observed until 100 days, where an almost constant value of 1.2 MPa is reached. The lowest swelling pressure magnitude is recorded at SP100 where a value of about 0.6 MPa is reached D r a f t 14 after 60 days and remains constant till 300 days. For all sensors, after 230 days a fluctuation is observed for all sensors, which could be due to problems in the system acquisition during the test.

For the axial swelling pressure, as in sample 1, it increases slowly and reaches a nearly stationary value after almost 100 days. However, in this case the magnitude of the swelling pressure in lower (1.1 MPa in this case against 2.3 MPa for sample 1).

Figure 12displays the profiles of swelling pressure at different times for both samples. For sample 1 (Figure 12a), the curves have a quasi-parabolic shape with a symmetry at 60 mm. This trend is not observed for the highly heterogeneous sample 2 (Figure 12b). In this case, the highest swelling pressure value is reached at the bottom of the mixture, in the denser part of the specimen. Figure 13 displays the swelling pressure profiles after 300 days of hydration for both samples. A relatively constant radial swelling pressure is observed for sample 1, in the range from 3.3 to 4.4 MPa. On the contrary for the highly heterogeneous sample 2, the swelling pressure tends to decrease from the bottom to top. The highest value is observed at 20 mm from the bottom hydration face, where the sample has a high initial density of 1.79 Mg/m 3 .

Hydraulic results

Figure 14 shows the evolution of Relative Humidity (RH) measured at different distances from the hydration faces for both samples. For sample 1 (Figure 14a), once hydration started, the relative humidity at RH31 located at 31 mm from the bottom hydration face and at RH111 located at 9 mm from the top hydration face increases rapidly and reaches 100% after 20 and 13 days respectively. At RH71, located at 49 cm from the top hydration face started to increase, relative humidity started to increase after about 45 days, indicating that the increasing rate of RH is dependent on the distance from the wetting ends.

D r a f t

For sample 2 (Figure 14b), two patterns of behavior can be distinguished. For sensors RH71, RH91 and RH111 located within the upper looser part of the sample, RH increases instantaneously at the beginning of the test, reaching 100% for RH91 and RH111 and 80% for RH71. Then, a decrease is observed for the three sensors until 92%, 87% and 58% for RH111, RH91 and RH71, respectively. Subsequently, RH increases again until saturation at 4, 30 and 40 days for RH111, RH91 and RH71 respectively. Different trends are observed at RH51 and RH 31 located within the denser bottom part of sample 2. At these sensors RH increases progressively until reaching 100% after 20 (RH 31) and 40 days (RH 51), respectively.

The volume of injected water is displayed in Figure 15 for both samples. The top and bottom volumes of injected water were measured separately during the saturation process. In both cases, a higher volume is injected though the top due to the presence of larger voids at this level combined with the gravity effect. Additionally, larger water volumes were injected through the top and bottom for sample 2.

After 300 days, the total volume of injected water is 184.6 cm 3 for sample 1 and 230.4 cm 3 for sample 2. These volumes are higher than the theoretical water volume (155 cm 3 ) calculated by considering a global dry density of 1.49 Mg/m 3 .

Comparative analysis and discussions

Since no information is available about the structural distribution of the bentonite pellet/powder mixture in SEALEX in situ test PTN4, comparison of the latter to both mock up tests, allows better interpretation of PTN4 results. The mock up tests are considered as extreme cases since they were performed on a highly heterogeneous sample (sample 2) and on an ideally prepared sample (sample 1) (specific preparation protocol to minimize the heterogeneities as much as possible).

Injected volume of water

D r a f t

The flooding phase in PTN4 was performed in 2 steps: First, a back pressure of 0.2 MPa was applied, in 2 hours. Second, the back pressure was removed, and the upper level of the water in the tank was constantly maintained at 1m above the axis of the borehole (i.e. 1 m water head). Figure 16 shows the evolution of injected water volume over time. Interestingly, a backward flow into the tank induced by bentonite swelling is observed. After a slight increase in rate during the first months, the water intake slowed down to a very low rate and reached 95 L after 780 days. A close examination of the hydration system revealed the presence of entrapped air within the hydration lines which prevented further water intake. Purging of the tubings allowed for further water intake. A total amount of 125 L of synthetic water was injected into the system after 1400 days.

In order to compare the evolution curves at different scales (PTN4 versus mock up tests), the water volumes were normalized considering the maximum water volume that can be injected in both mock up and in situ tests (158 l for PTN4, 0.158 l for mock up tests). Assuming that the in situ test is an intermediate case in between the two extreme cases studied in the laboratory (mock up 1 and 2), an up scaling time scale ratio of 30 (in situ/mock up tests) was found from the normalized water volume (Figure 17).

Even though both mock up test samples were prepared at the same mean dry density, larger injection rate and water volume were observed in sample 2. At the beginning of the test, the injection rate is higher for sample 2 because the permeability is higher in the looser upper part of the sample. The difference between injected water volume in sample 2 and the theoretical value may be related to the low water density (1.00 Mg/m 3 ) considered. This value can be much higher for high plasticity materials as the MX80 [START_REF] Marcial | Comportement hydromécanique et microstructural des matériaux de barrière ouvragée[END_REF][START_REF] Villar | Influence of temperature on the hydro-mechanical behaviour of a compacted bentonite[END_REF][START_REF] Lloret | Advances on the knowledge of the thermo-hydro-mechanical behavior of heavily compacted FEBEX bentonite[END_REF][START_REF] Jacinto | Influence of water density on the waterretention curve of expansive clays[END_REF].

D r a f t

Relative humidity evolution

In PTN4, 8 relative humidity sensors were installed in the bentonite core at 22, 52, 82 and 112 cm from the downstream hydration face (Figure 2). Sensors RH52 (1, 2) placed at 52 cm from the downstream lid was malfunctioning since the beginning of the hydration phase.

Additionally, data from several operating sensors were not available during several time periods due to a temporary problem in the data acquisition units. The variations with time of relative humidity measured at different sections are displayed in Figure 18. In situ data are compared to those from both mock up tests using time scale factor of 30. Once the hydration started, at sensors RH22 (1,2) and RH112 (1,2) located at 22 and 112 cm from downstream hydration face in PTN4, the relative humidity increased progressively and reached 95% after 450 days. A faster RH increasing rate is observed in both mock up tests at sensor RH22 located closer to the bottom and top hydration faces, respectively. This difference is mainly attributed to the different sensors positioning within the in situ and laboratory tests. Indeed, in the mock up tests RH sensors were located at the interface between the sample and the cell, where there is almost no bentonite powder filling the inter-pellets voids (Figures 6 and8), while in the in situ test RH sensors were installed in the bentonite-based core at a distance of about 12.5 cm from the host rock.

The µ-CT observations of sample 1 (Figure 6) revealed that after 56 days in the central zone of the sample pellets were clearly distinguishable, indicating that pellets swelling and hydration are still progressing. Nevertheless, RH measurements suggests full saturation of the mixture after almost 45 days ( Figure 14a). This indicates that the saturation of the highly compacted pellets (2.2 Mg/ m 3 ) is a slower process. This phenomenon was identified as water transfer between the macrostructure and the microstructure, the rate being controlled by several microstructural parameters (e.g. [START_REF] Gens | Hydro-mechanical Behavior of a Heterogenous Compacted Soil: Experimental Observations and Modelling[END_REF][START_REF] Alonso | Hydromechanical behavior of compacted granular expansive mixtures: experimental and constitutive study[END_REF].

D r a f t

Different evolution trends are observed at RH82 (1,2) (Figure 18c). Similarly to RH71 and RH 91 located within the looser upper part in sample 2, a fast increase of RH is observed at the beginning of hydration until reaching 100%, prior to a rapid drop. Then, RH increases again gradually. This suggests that the structural distribution of the bentonite-based core at the vicinity of the RH sensors is comparable to the upper looser part in sample 2. Since no powder exists in this zone, this behavior can be associated to structural changes of the granular pellets during wetting: initially, water comes instantaneously through the large interpellet voids, which leads to rapid RH increase. Simultaneously, there is a water transfer from the large inter-pellet voids to the highly compacted pellets voids. As pellets saturation progresses, they lose their initial granular structure due to swelling, and invade the inter-pellet voids (Figures 8 and9). The prevailing suction at the vicinity of the sensors is no longer the initial one measured just upon hydration but corresponds to that of the swollen pellets which is much higher. Saturation of the pellets is a slower process and RH increases again gradually.

Swelling pressure evolution

The in situ radial and axial swelling pressure evolutions are depicted in Figure 19and 20 respectively, together with the normalized curves of mock up tests. Figure 19 compares the evolution of PTN4 radial swelling pressure measured by three total pressure sensors installed on the surface of the bentonite core at 60 cm from the downstream saturation face. It is observed that the swelling pressures increased over time at different rates, depending on the sensors positions. The highest swelling pressure increasing rate is measured at sensor SP60

(1) located on the top of the radial cross section. The value reached at 1200 days is almost 2.4

MPa. On the contrary, the same lower swelling pressure evolutions rates are recorded at SP60

(2) and SP60 (3) and lower values are reached after 1200 days of hydration (1.3 MPa at SP60

(2) and 1.2 MPa SP60 (3)). The measured differences suggest a heterogeneous structural distribution within the in situ mixture core, resulting from the field installation process D r a f t adopted. This observation is confirmed when comparing the radial swelling pressures measured at the same position in both mock up tests: different swelling pressure increasing rates and magnitudes were measured even though both tested samples were fabricated at the same global dry density (1.49 Mg/m 3 ).

Figure 20 shows the evolution of axial swelling pressure measured by a sensor placed at the downstream hydration face in PTN4. No reliable data have been provided by the total pressure sensor at 120 cm, at the upstream hydration face. The plot shows that the swelling pressure increased at a constant rate and reached 1.4 MPa after 1200 days. Lower values are obtained in the laboratory mock up tests. This difference is closely related to the measurement methods. In the in situ test, the sensors placed at upstream and downstream measured the axial swelling pressures locally, while in the mock up tests, the force transducer installed under the cell base ensure the measurement of the global axial swelling pressure.

No peaks of swelling pressure are observed in the in situ test, while this particular behavior characterized by a peak occurrence followed by a decrease of the swelling pressure are observed in the mock up tests: at sensors SP100 (mock up 1) and SP20 (mock up 2), suggesting the appearance of local collapse of the macrospores between the bentonite grains in the latter case [START_REF] Alonso | Hydromechanical behavior of compacted granular expansive mixtures: experimental and constitutive study[END_REF][START_REF] Gens | Hydro-mechanical Behavior of a Heterogenous Compacted Soil: Experimental Observations and Modelling[END_REF]. The non-peak occurrence in PTN4 suggests that the sensors were placed in zones where high density pellets are tightly arranged with a few or no grains of bentonite powder in between.

For both in situ and laboratory tests, the final values of axial swelling pressure are lower than those measured radially. A high anisotropic coefficient C a (defined as the horizontal swelling pressure divided by the axial pressure) ranging from 1.25 and 1.65 is obtained for mock up 1 (Figure 21). This means that sample 1 is only homogeneous in terms of particle arrangement but anisotropy of the soil fabric is not encountered. For mock up 2 (sample 2), the values of C a range from 0.5 to 2.9. This strong anisotropy is obviously induced by the highly D r a f t 20 heterogeneous structural distribution in sample 2. Similarly in PTN4, significant Ca values are obtained (Figure 21), confirming that the installation process greatly influenced the structural distribution of the mixture. Different values are obtained for the three sensors located at 60 mm as their responses are different. The values are near one for SP60(1) and SP60(2) and between the two mock-up tests for SP60(3). This suggests that high heterogeneity exists in several zones PTN4, where pellets are assembled with no or few bentonite powder filling the inter-pellets large voids.

Conclusion

In the SEALEX project initiated by IRSN, the performance test PT-N4 was conducted to investigate the long term hydro-mechanical behavior of a mixture of pellet and powder of MX80 bentonite (80/20 in dry mass). In this test, the bentonite-based core was fully equipped with wireless sensors in order to measure the radial and axial swelling pressure as well as relative humidity at different positions during the saturation process. In parallel, 1/10 mockup tests were performed in the laboratory in order to further investigate the effect of the initial structural distribution of the mixture on the HM behavior.

Two different samples were tested, aiming at investigating different possible responses depending on the initial pellet/powder distribution. The first one consists of a pellet/powder mixture with a proportion of 80/20 in dry mass, fabricated with a special protocol which allows a relatively homogeneous sample. The second one was specially fabricated to investigate a strong heterogeneous distribution: the lower half of the sample was prepared with a mixture in which all the inter-pellet voids were filled with grains of powder, while the upper half of the sample was filled only with pellets of bentonite. A heterogeneous evolution of the swelling pressure was found for both mixtures, especially for the second one, for which different swelling pressure values were found after 300 days of hydration. This heterogeneity D r a f t 21 is due to the different initial dry density values in the vicinity of different sensors, which depends on the pellet/powder distribution. The final value of axial swelling pressure was different for both mixtures even though the global dry density was the same. This means that the axial swelling pressure depends also on the pellet/powder distribution. Therefore, special attention has to be paid to establish the relationship between swelling pressure and dry density of the material, in which the axial swelling pressure is usually considered. µ-CT observations carried out on the two samples while wetting revealed that an apparently homogeneous sample was obtained for the first mixture after 100 days of hydration. However, for the second specimen, several voids were still observed after 40 days of hydration, concentrated in the looser part of the sample. No voids were observed in the horizontal sections of the mixture near the hydration front for the looser part after 90 min of hydration.

Therefore, more time is necessary to reach a homogeneous sample for the second mixture.

A comparison was performed between the laboratory tests and the SEALEX in-situ test. A factor scale of 30 was considered while comparing both tests, based on the volume of water injected during wetting. The different protocols followed in order to obtain the in-situ mixture and the investigated material in the laboratory explains the differences between the results, especially for the axial swelling pressure.

Fig. 1 .Fig. 2 .Fig. 3 .

 123 Fig. 1. Layout of the SEALEX in situ test (Modified from Mokni et al. 2016) 341x168mm (72 x 72 DPI)

FigFig. 6 .Fig. 7 .Fig. 8 .Fig. 9 .Fig. 10 .Fig11.Fig. 12 .Fig. 13 .Fig. 14 .

 678910121314 Fig. 5. µ-CT observations of the investigated mixture for (a) mock-up 1 and (b) mock-up 2

Fig. 11 .Fig. 16 .Fig. 18 .Fig. 19 .Fig. 20 .Fig. 21 .

 111618192021 Fig. 11. Volumes of injected water into sample 1 and sample 2 while hydration.

Table 1 .

 1 Chemical composition of the synthetic water

	Components	NaHCO 3 Na 2 SO 4 NaCl KCl CaCl 2	MgCl 2 O6H 2 O SrCl 2 6H 2 O
				2H 2 O
	Mass (g) per litre	0.28	2.216	0.615 0.075 1.082	1.356	0.053
	of solution

https://mc06.manuscriptcentral.com/cgj-pubs Canadian Geotechnical Journal


Pierre Rouanet
email: pierre.rouanet@inria.fr

Pierre-Yves Oudeyer

David Filliat
email: david.filliat@ensta.fr

An integrated system for teaching new visually grounded words to a robot for non-expert users using a mobile device

In this paper, we present a system allowing nonexpert users to teach new words to their robot. In opposition to most of existing works in this area which focus on the associated visual perception and machine learning challenges, we choose to focus on the HRI challenges with the aim to show that it may improve the learning quality. We argue that by using mediator objects and in particular a handheld device, we can develop a human-robot interface which is not only intuitive and entertaining but will also "help" the user to provide "good" learning examples to the robot and thus will improve the efficiency of the whole learning system. The perceptual and machine learning parts of this system rely on an incremental version of visual bag-of-words. We also propose a system called ASMAT that makes it possible for the robot to incrementally build a model of a novel unknown object by simultaneously modelling and tracking it. We report experiments demonstrating the fast acquisition of robust object models using this approach.

I. INTRODUCTION

Social robots are drawing an increasing amount of interest both in scientific and economic communities. These robots should typically be able to interact naturally and intuitively with non-expert humans, particularly in the context of domestic services or entertainment. Yet, an important challenge needs to be addressed: providing the robot with the capability to adapt and operate in uncontrolled, novel and/or changing environments, in particular when interacting with non-expert humans. Thus, the robot should have the ability to discover its environment. Among the various challenges that this implies, we focus here on the problem of how a robot can learn through the interactions with the human and in particular, how a non-expert human can teach a new word to a robot typically associated with an object in its close environment.

However, in its full generality, this brings up very hard problems and in particular the issue of how the robot can infer the conceptual meaning of a new word [START_REF] Steels | Aibo's first words: The social learning of language and meaning[END_REF]. Here, we will restrict ourselves to the specific case where a given word is only associated with a single concrete object. Thus, we are not dealing with concepts, but only with visual object instance recognition. Nevertheless, this is a very ambitious project since several important obstacles still need to be crossed:

• Attention drawing: How can a human smoothly, robustly and intuitively draw the attention of a robot towards himself and towards the interaction when the robot is doing its own activity? How can the human draw the robot's attention even if he is not in its field of view?

• Pointing: How can a human designate an object to a robot and draw its attention toward this particular object? If the object is not in the field of view of the robot, how to push the robot to move adequately? When the object is within the field of view, how can the human point at this particular object and how could the object be robustly extracted from its background?

• Joint attention: How can the human understand what the robot is paying attention to? How can the human accurately know what the robot is seeing? How can joint attention be realized [START_REF] Breazeal | Infant-like social interactions between a robot and a human caregiver[END_REF][3]?

• Naming: How can the human introduce a symbolic form that the robot can perceive, register, associate with the object, and later on recognize when repeated by the human? What modaliti(es) shall be used to ensure ease of use, naturalness, and robustness?

• Categorization: How can associations between words and visual representations of objects be memorized and reused later on to allow the human to have the robot search an object associated with a word he has already taught to the robot? Like when human children learn language, social partners can only try to guide the acquisition of meanings but cannot program directly the appropriate representations in the learner's brain. Thus, the process of data collection may lead to inappropriate learning examples. False interpretations could ensue from a wrong data collection. How can we maximize the efficiency of example collection while keeping intuitive and pleasant interaction with non-expert humans? How can we recognize when two examples are related to the same object? Could the human help the robot during this process?

• Searching: How can a human intuitively ask for the robot to find or point to an already known object? How can easily and robustly the matching word can be recognized? How can the user intuitively help the recognition?

Thus, in order to give the ability to a non-expert human to teach new words to its robot, we have to address visual recognition, machine learning and also Human-Robot Interaction (HRI) challenges. In this paper, we argue that by focusing on the HRI challenges we could significantly improve the whole learning system. We therefore propose a system to tackle some of these challenges (attention drawing, pointing, joint attention and naming) and illustrate the improvement in the efficiency of the learning system brought by this approach.

II. RELATED WORK

Over the past few years, some works tried to address these issues by transposing human-human modes of interaction. Scassellati developed mechanism of shared attention through gaze monitoring and pointing gesture [START_REF] Scassellati | Mechanisms of shared attention for a humanoid robot[END_REF]. In his work, he used a fixed upper-torso humanoid robot. Many researches also tried to recognize pointing gestures in order to designate objects to a robot [5][6]. Steels et al. developed a complete social framework based on direct interactions (pointing gestures and speech recognition) to allow an user to teach words to an AIBO [START_REF] Steels | Aibo's first words: The social learning of language and meaning[END_REF]. In this work, the authors are more specifically focusing on the visual perception and machine learning issues. Yet, the HRI was identified has a major limitation of their system. In particular, they showed that the lack of robustness of the interface lead to some bad learning examples and so decreased the learning performance [START_REF] Kaplan | Les machines apprivoisées comprendre les robots de loisir[END_REF]. Thus, in this paper we are proposing an integrated system allowing to teach new words to a robot, with a special attention on the HRI challenges. More specifically, we tried to develop an intuitive, efficient and entertaining interface, which also makes it possible for the user to provide the system with good learning examples. By doing so, we are hoping to improve the performance of the whole learning system.

As presented above, in most of the related works, authors choose to use the direct interactions (gesture recognition, gaze tracking or voice recognition) to try to address the above mentioned HRI challenges. In particular, this approach potentially provides really natural interactions which is particularly important with non-expert users. Unfortunately, existing associated techniques are not robust enough in uncontrolled environments (due to noise, lighting or occlusion) and most social robots have a body whose shape and perceptual apparatus is not compatible with those modes of interaction (small angle of view, small height...). This implies that such an approach will fail if one is interested in intuitive and robust interaction with non-expert users in unconstrained environments.

We argue that one way to help to achieve intuitively and robustly some of the functionalities presented above is to develop simple artefacts that will serve as mediators between the human and the robot to enable natural communication, in much the same way as icon based artefacts were developed for leveraging natural linguistic communication between human and certain bonobos [START_REF] Rumbaugh | The Ape at the Brink of the Human Mind[END_REF]. More particularly, we argue that using mobile devices, such as illustrated in figure 1 may enable to circumvent some of theses problems. Though it may seem less natural to use a device as a mediator Using a handheld device as a mediator object to control the movements of a social robot.

object between humans and robots, by allowing a robust, reliable and working interaction, it may lead to actually more practical and usable interactions. Such interfaces may provide pleasant and nonrestrictive interactions, and so rather quickly become sort of "natural" interactions.

These kinds of interfaces have already been used to interact with a robot. Kemp. et al have shown how a laser pointer can be intuitively used by people with severe motor impairments to robustly designate objects to a robot [START_REF] Choi | Laser pointers and a touch screen: intuitive interfaces for autonomous mobile manipulation for the motor impaired[END_REF]. Thanks to the laser spot light, the human can also accurately know what he is pointing at. Yanco et al. used an interface based on an input device (touch screen or joystick) to select objects which will be grasped by a wheelchair mounted robotic arm [START_REF] Tsui | Development and evaluation of a flexible interface for a wheelchair mounted robotic arm[END_REF]. In their work the user can directly monitor the object selection on the screen of the device. As we try to do in our system, they both can draw the robot attention toward objects and so realize joint attention between the human and the robot. However their robot is able to automatically grasp the object from a detected 3D spot, in a framework that requires image segmentation algorithm and/or a priori objects knowledge. If objects are not known beforehand these are still hard problems.

In order to circumvent this problem, we argue in this paper that is possible to have the user segmenting himself the object from the image in an intuitive manner by using a handheld touch-screen device. Indeed, the screen of the device can be used to provide the human with information about what the robot is perceiving, which is interesting with non-expert users who are particularly proned to make assumptions about the capacity and behavior of the robot. But it also allows to transfer information from the human to the robot, through easily perceivable gestures [START_REF] Skubic | Hand-drawn maps for robot navigation[END_REF]. Moreover, these sketches and gestures are natural cues and so are natural for people to use [START_REF] Goodrich | Seven principles of efficient human robot interaction[END_REF]. Thus, we can develop intuitive collaborative interaction allowing the human to supervise the robot and allowing the robot to take advantage from the human capabilities [START_REF] Fong | A personal user interface for collaborative human-robot exploration[END_REF].

In particular, here we can display the camera stream on the screen and let the user to encircle the interesting object on the touch-screen. Finally, handheld devices allow the human to be next to the robot and physically engaged, for example allowing to catch objects and waving them physically in the robot's field of view. They also allow tele-interaction with the robot through the video feedback of the camera.

Other handheld device based interfaces have been developed recently. For instance, Fong et al. used a PDA for remote driving [START_REF] Fong | Pdadriver: A handheld system for remote driving[END_REF], and Kaymaz et al. used it to tele-operate a mobile robot [START_REF] Kaymaz | Pda-based human-robotic interface[END_REF]. Sakamoto et al. showed how they can control a house cleaning robot through sketches on a Tablet PC [START_REF] Sakamoto | Sketch and run: a stroke-based interface for home robots[END_REF]. However, to our knowledge nobody used a handheld device for such teaching interactions. We already proposed a prototype based on a handheld device to teach new words to a robot [START_REF] Rouanet | Exploring the use of a handheld device in language teaching human-robot interaction[END_REF]. This prototype was developed with a special care to the classical design lessons in HRI and HCI [START_REF] Adams | Critical considerations for human-robot interface development[END_REF][19] [START_REF] Yanco | Beyond usability evaluation: analysis of human-robot interaction at a major robotics competition[END_REF]. The exploratory study indicated that it was a promising approach, providing an intuitive and efficient interface for non-expert users. We also compared different interfaces for showing object to a robot and concluded that the gesture interface based on a handheld device was stated as the most satisfying by the users [START_REF] Rouanet | A comparison of three interfaces using handheld devices to intuitively drive and show objects to a social robot : the impact of underlying metaphors[END_REF]. In this paper, we propose a fullsystem with advanced visual perception, machine learning and HRI components.

III. OUTLINE OF THE SYSTEM

A. Visual perception

We adopted the popular "bags of visual words" approach to process images in our system. Bags of visual words is a method developed for image categorization [START_REF] Csurka | Visual categorization with bags of keypoints[END_REF] that relies on a representation of images as a set of unordered elementary visual features (the words) taken from a dictionary (or code book). Using a given dictionary, a classifier is simply based on the frequencies of the words in an image, thus ignoring any global image structure. The term "bag of words" refers to document classification techniques that inspired these approaches where documents are considered as unordered sets of words. Several applications also exist for robotics, notably for navigation (e.g. [START_REF] Wang | Vision-based global localization using a visual vocabulary[END_REF], [START_REF] Angeli | Real-time visual loop-closure detection[END_REF]).

The words used in image processing are based on local image features such as the SURF keypoints [START_REF] Bay | Speeded-up robust features (surf)[END_REF] we are using in this paper. As these features can be noisy and are represented in high dimension spaces, they are categorized using vector quantization techniques (such as k-means) and the output clusters of this categorization are the words of the dictionary. Instead of building the dictionary off-line on an image database as is performed in most applications, we use an incremental dictionary construction ( [START_REF] Filliat | Interactive learning of visual topological navigation[END_REF]) that makes it possible to start with an empty dictionary and build it as the robot discovers its surroundings. This make it possible to learn objects without any a priori on the object type or the environment of the robot.

This model has two interesting characteristics for our application: the use of feature sets make it robust to partial object occlusions and the feature space quantization bring robustness to image noise linked to camera noise or varying illumination. More over, with the incremental dictionary construction, this quantization is adapted as the environment evolve (for example when light change from natural to artificial).

B. Machine learning

For our application, the classifier designed for object recognition should be trained incrementally, i.e. it should be able to process new examples and learn new objects without the need to reprocess all the previous data. To achieve that, we use a generative method in which training entails updating a statistical model of objects, and classifying entails evaluating the likelihood of each object given a new image.

More specifically, we use a voting method based on the statistics of visual words appearance in each object. The recorded statistics (according to the learning method described later) are the number of appearance a wo of each visual word w of the dictionary in the training examples of each object o. For object detection in a new image, we extract all the visual words from this image and make each word w vote for all objects o for which a wo = 0. The vote is performed using the term frequency-inverted document frequency (tf-idf) weighting [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] in order to penalize the more common visual words. An object is recognized if the quality of the vote result (measured as the difference between the best vote and the second best) is other a threshold.

Estimating the statistics a wo require the labelling of examples with the associated object name. As will be described later, we will use two methods for example labelling depending on the information given by the user : labelling the whole image or labelling only an image area given by the user and representing the object . Additionally, we will propose a new scheme for automatically labelling new examples from an initial user labelled example (see section III-D).

C. Human Robot Interaction

We choose to use the Nao robot as our test platform. Indeed, to us it well represents the present of social robotics: with a toy-aspect and classical inputs (camera, microphone). Furthermore, it is a biped robot and it has a complex skeletal so it leads to complex motions. Finally, as it is a humanoid, user will probably be more proned to teach it new words.

Our system was embedded on an Apple iPhone used as a mediator object between the human and the robot. We choose this device because it allows to display information on the screen to the user and also allows to interact through "natural" gestures through a large amount of possibilities due to the multi-touch capacities. Moreover, thanks to the large success of the iPhone we can take advantage of a well-known interface, allowing ease of use.

In this system, the screen of the handheld device displays the video stream of the robot's camera (at about 15 fps). It accurately shows what the robot is looking at, which can thus be monitored by the user allowing to resolve the ambiguity of what the robot is really seeing (see figure 2). As mentioned above, achieving such an ability with direct interaction is difficult with personal robots such as Nao humanoid or the AIBO robot due to the specific morphology of such robots and the particular characteristic of their camera, in particular with non-expert users who are specifically proned to make really ambitious assumptions about the robot capacity. Moreover, having a visual feedback seems to entertain the user [START_REF] Rouanet | A comparison of three interfaces using handheld devices to intuitively drive and show objects to a social robot : the impact of underlying metaphors[END_REF], while the robot is moving for instance. Fig. 3.

Drawing attention towards an object: the user first sketches directions to position the robot such that the object is in its field of view (left), and if he wants to center the robot's sight on a specific spot just tap on it (right).

When the human wants to draw the robot attention toward an object which is not in its field of view, the user can sketch on the screen to make it move in an appropriate position: vertical strokes for forward/backward movements and horizontal strokes for right/left turns. Elementary heuristics are used to recognize these straight sketches. The moves of the robot are continuous until the user re-touch the screen in order to stop it. Another stroke can directly be drawn to go on the next move (for instance, go forward then directly turn right). Pointing on a particular point on the screen makes the robot look at the corresponding spot (see figure 3).

When the user wants to show an object which is in the field of view of the robot, and thus on the screen, in order to teach a name for this object, it sketches a circle around this object on the touch screen (as shown on figure 4). Circling is a really intuitive gesture because users directly "select" what they want to draw attention to. This gesture is particularly well-suited to touch-screen based interactions. For instance, Schmalstieg et al. used the circling metaphor to select objects in a virtual world [START_REF] Schmalstieg | Using transparent props for interaction with the virtual table[END_REF]. As for the straight strokes, heuristics are here used to recognize circular sketches, based on the shape of the stroke and the distance between the first and the last point of the sketch. This simple gestures has two important functions: Fig. 4. Encircling an object allows the user to notify the robot that he wants to teach a name for this object. But it also provides an useful rough object segmentation.

• First, it allows to clearly separate, by using two different gestures, the action of drawing the robot attention toward an object and the user's will of teaching a new word for this object.

• Second, circling is also a crucial help for the robot since it provides a rough visual segmentation of the object, which is otherwise a very hard task in unconstrained environments. With the stroke and the background image, we can extract the selected area and define it as our object's image. Classical computer graphics algorithms are used to compute this area (Bresenham line drawing and flood fill).

Fig. 5. Some objects can not be segmented with the classical object segmentation algorithms. For instance, on the left the object has almost the same color than the background. In the middle example, the object is not movable and so can not be segmented with the motion based segmentation method. On the right example, an automatic method can not guess if the user wants to show only the head of the giraffe or the whole poster. Furthermore, this is a 2D object and so the range method can not deal with it.

In this paper, we argue that object segmentation is still a hard task in unconstrained and unknown environments. Different approaches have been developed over the past years to address this problem. However, they are still suffering from a lack of robustness. For example, Region growing algorithms try to address this problem by determining regions where color or texture are homogeneous. These region are iteratively expanded from a seed [START_REF] Adams | Seeded region growing[END_REF]. Yet, these algorithms can not deal with complex objects made up of several subparts with various colors and textures. Moreover the colors or textures of the object can also be similar to the background (examples are shown on the figure 5). A lot of researches have also studied how the boundaries of the object could be determined through its motion : motion based segmentation [START_REF] Arsenio | The whole world in your hand: Active and interactive segmentation[END_REF]. Although, this algorithm can only segment carryable or movable object. Range segmentation algorithms use images containing depth information to compute regions belonging to the same surface [START_REF] Bab-Hadiashar | Range image segmentation using surface selection criterion[END_REF]. Of course, this approach does not allow to recognize 2D objects such as a poster. By asking the user to segment the image with a circling stroke, we circumvent all the above mentioned problems and we can deal with all the kind of objects, allowing us to work in unconstrained environments. Fig. 6. When a joint attention situation has been achieved on an object, a set of actions are presented to the user, which can in particular decide to teach a name for this object Once this object is encircled, a menu pops-up showing several interaction options : naming, guessing or high-level actions such as approach, as drawn on figure 6. The "name" choice makes the system wait for the user to enter a referent. In this prototype, we choose to enter the name through a virtual keyboard which allows us to quickly have a whole working system (see figure 7). Moreover, it also allows to circumvent the name recognition problem. Obviously, in further work other modalities such as vocally naming will be used. Once the user has entered the word he wanted to teach, the visual features inside the circle are added to our learning system and the corresponding words are labelled as belonging to the model of the object (as described in section III-B). Fig. 7. When the user has decided to teach a name for an object, the system is waiting for him to enter a word with the virtual keyboard.

Later on, when the robot has learnt some words, the user can ask it to look for one of this object by entering the looking menu and selecting the object he wants the robot to look for, as shown on figure 8. A simple search algorithm have been developed to move the robot until it detects the searched object by using the visual bag-of-words system. Fig. 8. Once the robot has learnt some words, the user can ask the robot to look for one, by selecting it on the looking menu.

D. Active simultaneous modelling and tracking : ASMAT

As presented above, each time the user encircles an object, our system only improves the model of the object with a single image. However, the appearance of an object can drastically changes from different points of view, so multiple images of an object are needed in order to be able to recognize it, in spite of the point of view [START_REF] Robbel | Exploiting object dynamics for recognition and control[END_REF]. So we develop a system allowing to automatically collect many learning examples, with different points of view, from a single user's intervention (see figure 9). Through this approach, we think we can get a much more robust model of an object and provide a less restrictive interaction to the users. Fig. 9. We can automatically extract image of an object from different points-of-view with our active simultaneous modelling and tracking (AS-MAT) system in which the robot turns around the objects, and thus improve the model of the object and make it more robust.

With this system, when the user encircles an object on the screen of the device, we make the robot turn around the object and take pictures of the object from different points of view. However, this a hard problem because we do not have any a priori model of the object in order to track it, and to get a model of the object we need to be able to track it. In order to achieve such an ability we need to be able to simultaneously model and track an object without any prior data. A related system was already presented by Dowson et al. [START_REF] Dowson | Simultaneous modeling and tracking (smat) of feature sets[END_REF], but using previously recorded video, while we can here directly define the movements of the camera. For this reason, we called our system active simultaneous modelling and tracking (ASMAT). With this system, the robot incrementally constructs a model of the object, thanks to the incremental bag-of-words method which is used at the same time to robustly track the object and thus to enable the robot to turn around it.

The robot incrementally turns around the object through N steps. The robot goes from one position to the next one by moving sideways and turning itself in order to keep the tracked object in the center of its sight. For each stationary position, we lightly move the head of the robot to quickly get M different images of the object. For each image, we first find the SURF keypoints (in order to work in real time) matching the model of the object. We compute the gravity center of these points and compute the average distance to this center. We then filter the points which are too far from this average region. Finally, we define the bounding box of the elected points as the object, i.e. we add to the model of the object all the keypoints inside this box (see algorithm 1).

Such an approach allows the learning system to quickly get a robust model of the object (as shown in the experiments in section IV-B). However, this system can lead to exponential deviation due to fact that the constructed model is also used to track the object, thus the more the system is mistaken more he will be mistaken. To circumvent this problem, we could display on the screen of the iPhone the bounding box of the tracked object allowing the user to stop the robot as soon as it goes wrong and for instance, ask the user to re-encircle the object in order to restart the process. However, this possibility has not yet been implemented and thus evaluated.

IV. EXPERIMENTS A. Encircling

In order to test our integrated recognition system (visual perception, machine learning and HRI), we needed to recre-ate a realistic test environment. First of all, we characterize the environment and the kind of object the humans would teach to robot in a home environment. To us, these objects will probably be everyday objects which can be found for instance in a living room. We can then define two main groups of objects:

• small, light and carryable objects as a newspaper, keys or a ball

• bigger, fixed objects as a plant or a plug

We think that the first categories will represent the most important part of taught objects. Furthermore, they also probably are the hardest to recognize due to the background changes. To our knowledge, the most matching database would be the ETH-80 image set. However, this database uses class object and not only instances. Furthermore, a neutral background is used which is not representative of unconstrained environments. The point of view is also quite similar from one image to another, while the robot will have to recognize an object from different points of view. Thus, we decided to create our own database with a special attention to our criteria. We chose 20 different everyday objects, which are rather small, carryable and well-textured. As our objects are carryable, we must be able to recognize them in spite of their location, i.e. with different backgrounds. So we chose five different backgrounds (on the ground, on a desk, at a window, in front of a bookcase and in the kitchen) and took two pictures by object and by background. So, finally we got 10 images by object taken with different points of view but at the rather same height (figure 10). Every images has been roughly segmented with a stroke encircling the object.

Our database is deliberately rather small because we want to be able to recognize an object with few learning examples provided by the user. To us, ten user's examples seems to already be a maximum in order to keep an nonrestrictive interaction. The image were taken and encircled by the authors. Furthermore, they were taken with the camera of the iPhone, and converted to a low resolution (320x240) to correspond to a common resolution.

Fig. 10. Several examples of a specially constructed objects database corresponding to the kind of objects we think an human would like to teach to its robot (everyday object, small and carryable). The images were taken with different backgrounds in order to be able to recognize objects in spite of their location.

In order to test our recognition system, we use the following experimental protocol.

• We randomly choose N images per object.

• We train our learning system with these images.

• We test our learning system with the other images ( 10-N images per object).

• The test is repeated 20 times in order to circumvent the randomize effect.

• The final results are the mean recognition rate of each test. As shown on the figure 11, encircling the objects allows us to improve the recognition rate by 20% in average. So, we can see that we the recognition rate maximum (about 80%) was reached by giving six encircled learning examples, while the maximum with nine full images was not reached. By simply encircling the objects on the screen, the user can improve our recognition system and in particular can achieve robust recognition with fewer learning examples. Thus, we can reduce the number of user's interventions. Fig. 11. We can notice that with the encircled images, we have a recognition rate superior of 20% in average than with the full image. Thus, we reach the maximum recognition rate faster (from the sixth encircled example with encircled image while it is still not reached with nine full learning examples). However, our images were here not gathered in "real" conditions. Indeed, the images were not taken with a robot by non-expert users. Nevertheless, we try to reproduce a plausible interaction, by taking pictures with possible angles of view, with a common resolution for camera... Furthermore, the encircling was done by the authors. Thus, we should, in a future experience, test if non-expert users would provide as good inputs, as were given by the expert users and see if we can get the same results.

B. ASMAT

As mentioned above, we developed a system to automatically get a larger set of learning examples by making the robot turn around the objects. We try to evaluate the impact of such a method on the learning process. So, we designed an experiment where a user taught four different objects to the robot by encircling them on the screen of an iPhone. Then, the robot automatically turned around the object. At each step, the robot moves sideways and forward. Then it turns in order to recenter its sight on the center of the tracked objects. Then we take five snapshots with lightly different positions of the head. We repeat this operation five times, so 25 images were taken by learning example. For each object, the user give five different learning examples with different points of view of the object. We define two conditions:

• In condition A, we only use the first image (the one encircled by the user) to train our recognition system.

• In condition B, we use all the 25 images labelled using our ASMAT system to train the recognition system. We then used a similar database as the one used above (5 backgrounds, 2 images per background and per object), with our four objects to test the quality of the learning.

As we can see on the figure 12, with the condition A, we have a linear progression of the recognition rate according to the number of learning examples : with five learning examples we reached about 60% of recognition. With the condition B, we can notice a really fast increase of the recognition rate. A maximum (about 80%) is reached from the second learning examples. We can also notice that this maximum is not reached with the condition A even after the fifth examples. Thus, the ASMAT system seems to allow the getting of a robust and reliable model of an object with really few user's interventions. 12. Recognition rate according to the number of examples given by the user. We define two conditions, one with the ASMAT, the other without it. We can notice that by using this system, we can get a more accurate model of an object with fewer user's learning examples.

V. CONCLUSION AND FUTURE WORKS

A. Conclusion

Our proposed system, based on a combination of advanced HRI, visual perception and machine learning methods, allows non-expert users to intuitively and robustly teach new words to their robot. By using the touch-screen to transfer information, we have developed collaborative interactions, improving the mutual understanding between the robot and the human. We also showed that the interface may help the user to provide good learning examples which will thus improve the whole learning system.

B. Future works

In future works we will try to evaluate the impact of different interfaces on the learning by designing a comparative user's study with different kind of interfaces (with an iPhone, with a laser pointer and with direct interactions). We will compare them with a "learning quality" measure, but also with satisfaction questionnaires to assess their usability. It would also be interesting to evaluate the impact of the ASMAT system on the user's experience, especially with non-expert users. Thus, it could enhance the interaction, by making it more lively and more entertaining for the users. On the other hand, the extra time taken to do the active learning, may fatigue users.

Then, we will use a vocal naming system and so we will have to be able to compare two vocal words. We will also allow the user to improve the learning through collaborative interactions, where he could help the clustering of the different learning examples, and try to evaluate the real impact of such a feature.

Fig. 1 .

 1 Fig. 1.Using a handheld device as a mediator object to control the movements of a social robot.

Fig. 2 .

 2 Fig. 2. We display the video stream of the camera of the robot on the screen. This allows to accurately monitor what the robot is seeing and thus really achieving joint attention situation.

Algorithm 1

 1 ASMAT(user encircled image) keypoints ← extract keypoints(user encircled image) update object model(keypoints) while not user stop() and i < N do for j in 1 to M do move robot head() keypoints ← extract keypoints(robot camera) matches ← f ind matching object model(keypoints) elected ← f ilter isolate points(matches) bb ← compute bounding box f rom points(elected) for each kp in keypoints inside bb do update object model(kp) end for end for walk step around object() keypoints ← extract keypoints(robot camera) matches ← f ind matching object model(keypoints) center ← compute gravity center(matches) robot center sight(center) i ← i + 1 end while

Fig.

  Fig.12. Recognition rate according to the number of examples given by the user. We define two conditions, one with the ASMAT, the other without it. We can notice that by using this system, we can get a more accurate model of an object with fewer user's learning examples.

VI. ACKNOWLEDGMENTS

The authors would like to thank Jérôme Béchu for his implication in the development and the realization of the different parts of the system.