Heesun Choi

Cindy Crump

Christian Duriez

Asher Elmquist

Gregory Hager

David Han

Frank Hearl

Jessica Hodgins

Abhinandan Jain

Frederick Leve

Chen Li

Franzi Meier

Dan Negrut
email: negrut@wisc.edu

Ludovic Righetti

Alberto Rodriguez

Jie Tan

Jeff Trinkle

C And Duriez

D Fox

D Koditschek

Koenig

On the use of simulation in robotics: opportunities, challenges, and suggestions for moving forward

Keywords: Robotics, Simulation, Design, Controls, Machine Learning

The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-inrobotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic of hand. The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.

D R A F T

1. Generate, expeditiously and at low cost, large amounts with endowing the robot with smarts to actually carry out the tasks that it has the potential to execute. For both mechanical and control policy design, one typically produces prototypes that iteratively improve on previous versions until a certain prototype is acceptable; this prototype becomes a candidate solution. The iterative process to produce the candidate solution is time consuming. Additionally, it can be expensive, unsafe (for humans or the robot hardware) and sometimes impractical (if designing a rover for Mars, testing cannot be done in Martian conditions). Moving from a model-free to a model-based design approach; i.e., carrying out the iterative loop in simulation (8), can reduce the time associated with the design process. Indeed, changing a rover suspension design to assess trafficability in simulation can be as fast as modifying a handful of parameters in a template file that defines the geometry of the vehicle (9). By comparison, physically modifying the suspension of a prototype is significantly more time consuming. Additional time and cost savings are incurred in the subsequent stage, the testing of the candidate design. Before it becomes the solution, the candidate design is subjected to extensive additional testing in which a limited collection of candidate design clones are assessed via a predefined evaluation process. Physically building the collection of candidate designs is time consuming and costly since there is no assembly line ready yet and each clone is handcrafted. In simulation, testing the candidate might be as simple as copying the model files to different folders and conducting the predefined evaluation process using high throughput, parallel computing.

Opp.3. Provide an accelerated, safe, and fully controlled virtual testing and verification environment. Approaches to verification of autonomous systems are in their infancy (10). Approaches to verification and "debugging" D R A F T of autonomous robotic systems that learn on-line are essentially non-existent. Repeatability, particularly with respect to stress/corner cases; full control of the experiment insofar as the "environment" is concerned; and the lack of risk to human and hardware damage are three attributes that can make simulation instrumental in establishing principled protocols for autonomous system verification and, by extension, industry standards and guidelines. Against this backdrop, as autonomous systems cannot foresee unknowns that they may encounter, novel formal verification schemes can be developed to verify specifications such as safety in real-time under assumed uncertainty in the system and its environment (11).

Simulation can play an important role in providing insights into multi-robot, collaborative scenarios (12, 13). Collaborative, multi-robot systems can exclusively comprise robots interacting with each other based on their own local decisionmaking algorithms that factor in sensed and/or shared information; or, can include human interaction as in, for instance, search and rescue scenarios. As the number of robot-robot and/or human-robot interactions increases, so does the complexity of designing and verifying these systems. Physical testing and verification are daunting as the collection of scenarios to probe increases quadratically with the number of agents in the system. Moreover, it is difficult to systematically test in real conditions multi-robot systems used, for instance, in environmental monitoring, off-road mobility/survivability, surveillance or infrastructure management, etc., due in part to the stiff challenges posed by operating groups of agents in such environments. Simulation is very convenient in such scenarios, given that it can also be used to probe for inter-agent connectivity failure or hostile network penetration. Hostile/adversarial attacks aim at more than multi-robot network penetrationthe robot's control stack and sensing system are two other targets (14). One of the most compelling cases for use of simulation in robotics can be made in conjunction with the need to thoroughly check the consequence of such attacks. Do the checks designed to guard against adversarial attacks work as expected? What is a safe way to counteract an adversarial attack? The answer to these and similar questions is critical for safety and regulatory purposes yet direct hardware testing is costly and potentially unsafe (bulky robots gone awry can become outright dangerous). Simulating the response of the robotic system in cases of failure, while potentially performing a task, permits the investigation of fail-safe strategies for the system as well as the design of security, anti-breach policies.

Without simulation, one is limited in what and how many scenarios can be tested.

Opp.4. Facilitate the development of more intelligent robots. In this argument, the intelligence metric is tied to the breadth of scenarios in which the robot is expected to successfully fulfill a set mission. By this metric, industrial robots are at one end of the spectrum. They operate in very structured and static environments (known ahead of time) and are trained to perform a narrow and a priori established catalog of operations. The opportunity to develop more intelligent robots is a consequence of several technological advances: machine learning techniques opened the door to new control policies (3); the compute speeds available on an affordable power budget have increased substantially; sensing is sharper and more affordable; actuation is gradually becoming more sophisticated; low latency communication/networking and edge computing allow for real-time, just-in-time off-loading of heavy-179 duty computation during operation. Against this backdrop, 180 simulation can be a catalyst for the next stage in the evolution 181 of smart robots, one in which they operate in unstructured 182 environments and engage in decision-making activities. Just 183 as the concepts of morality (set rules), prior experience and 184 ability to predict the consequence of one's actions shape the 185 decision-making process in humans, a set of rules (includ-186 ing ethical) and an ability to predict consequences/outcomes 187 through on-line simulation can shape the decision-making pro-188 cess of robots through introspection (15). A rule that states 189 "plates shall not drop on the floor" combined with the ability to 190 run thousands of parallel simulation scenarios faster than real 191 time should determine the robot to place on a table the plate 192 at the top of the stack that it carries rather than one from 193 somewhere in the middle of the stack. The ability to foresee 194 on-line the outcome of a possible action through the lens of 195 simulation is anchored by spectacular gains in computation 196 speed. For instance, recent GPU cards can manage the simul-197 taneous execution of more than 150,000 computing threads 198 (16). With simple enough simulation kernels, this translates 199 into peeking into the future via as many possible scenarios, 200 leading to introspective control policies that complement the 201 established model predictive control approach and more recent 202 techniques that rely on on/off-line machine learning solutions 203 (17).

204

Opp.5. Facilitate the understanding of human-robot 205 interactions (HRI). The ability to simulate the interaction 206 between the robot and human opens the door to experimen-207 tation with tele-surgical robotics in semi-autonomous or au-208 tonomous operation; reducing risks to individuals in dangerous 209 work environments or working in cooperation with collabo-210 rative and mobile robots in a shared workplace; eliminating 211 or reducing repetitive motion trauma and musculoskeletal 212 overload; reducing fatalities and injuries from motor vehicle 213 incidents as autonomous vehicles are yet another form in which 214 robots interact with humans. The human-robot interplay can 215 project specialized medical expertise and care to any point 216 of need, at any time. This is of particular interest for teams 217 operating remotely, e.g., small teams of first responders, teams 218 of astronauts, etc. Finally, one important facet of the science 219 of HRI pertains to the aspect of establishing trust between hu-220 man and robot in shared decision-making [START_REF] Fong | A survey of socially interactive robots[END_REF]. Development 221 of simulation tools that better represent the psycho-social 222 nature of HRI and enable a common operating "picture" of 223 possible solution sets for decision-making may foster trust and 224 establish a baseline for more effective collaboration [START_REF] Metcalfe | Building a framework to manage trust in automation in Micro-and 904[END_REF]. day, satellite position, terrain properties, etc.), communication types and protocols, control policy type, etc. The scenario modeling abstractions that are needed should be conducive to yielding models with hierarchical structure that accommodate the user's requirements vis-à-vis the level of fidelity rendered in simulation -from less accurate but expeditious, to highly detailed/accurate but computationally more demanding. Gazebo [START_REF] Koenig | Design and use paradigms for Gazebo, an open-source multi-907 robot simulator[END_REF][START_REF] Aguero | Inside the virtual robotics challenge: Simulating real-time robotic 909 disaster response[END_REF] has made good strides in this direction, continuing in the tradition of previous tools like USARSim [START_REF] Carpin | USARSim: a robot simulator for research and education[END_REF]. However, the ontology associated with Gazebo's modeling approach and other similar efforts ( 23) is limited in scope. Dwelling on the Gazebo example, the platform exposes one simulation interface to several engines: ODE [START_REF] Smith | Open Dynamics Engine[END_REF], Bullet [START_REF] Coumans | Pybullet, a python module for physics simulation for games[END_REF], DART [START_REF] Lee | DART: Dynamic animation and robotics toolkit[END_REF], and Simbody [START_REF] Sherman | Simbody: multibody dynamics for biomedical research[END_REF]. Presently, the Gazebo simulation-in-robotics ontology does not include concepts such as "fluid viscosity", "material dissipation model", "soil plasticity", "Drucker-Prager yield", etc. since none of the Gazebo-exposed physics engines supports fluid dynamics, plasticity in soils, finite element analysis, etc. There are simulation engines that have started to support multi-physics with various degrees of success [START_REF]PhysX simulation engine[END_REF][START_REF] Tasora | Chrono: An open source multi-physics dynamics engine in High Performance Computing[END_REF] yet these engines presently come short on exposing roboticsfocused modeling abstractions. In moving forward, lessons can be learned from software platforms used to create computer games [START_REF]PhysX simulation engine[END_REF][START_REF]Unreal Engine[END_REF][START_REF]Main Website[END_REF]. These video gaming platforms are used with good success by several robotics simulation engines, e.g., [START_REF] Carpin | USARSim: a robot simulator for research and education[END_REF][START_REF] Dosovitskiy | CARLA: An open urban driving simulator[END_REF][START_REF] Shah | Airsim: High-fidelity visual and physical simulation for autonomous vehicles in Field and service robotics[END_REF] to create complex virtual worlds with visually appealing graphics. However, the gaming platforms are not designed to accurately simulate the physics governing the behavior of robots and their interactions with the environment. Consequently, their predictive attributes are limited owing to an emphasis on plausibility rather than accuracy.

Issue 3. Model composability needs further improvements. A rich universe opens up when multiple agents participate in the same scenario, see, for instance [START_REF] Simmons | Coordination for multi-robot exploration and mapping in Aaai/Iaai[END_REF]. Composability refers to the ability to combine, LEGO style, basic constructs/sub-models into complex simulation scenarios. In this approach, complex scenarios are investigated via composed models that draw on validated, discipline-oriented, sub-models. This calls for more than just the ability to build on a hierarchical representation of a model. Indeed, there is an interplay at run-time between these sub-components that takes place in the presence of dynamic (evolving) environments and human participation. For instance, the sub-models communicate with each other (agent-to-agent, agent-to-infrastructure, etc.) and interact with each other through contact, friction, visual or auditory cues, etc. These aspects need to be supported in a physics-based fashion by the simulation engine, and this is what makes this composability problem more than a "model representation"; i.e., a pre-processing, problem. One additional layer of complexity comes into play if a real time constraint must be met for scenarios that include numerous robots. Composability with scalability typically calls for simulators that rely on parallel computing, which further complicates the problem at hand due to time and space coherence requirements -robots should propagate their dynamics in the same world time and be able to sense each other although they might run on different hardware assets linked through a fast interconnect fabric. There are several sources of inspiration for handling composability, e.g., V-REP [START_REF] Rohmer | V-REP: A versatile and scalable robot simulation framework in[END_REF] dynamics, e.g., stick-slip conditions, impact, control policies that are nonsmooth, event-triggered changes that occur sporadically to suddenly change loads or boundary conditions, etc. A perspective that might be useful is a statistical one, in which confidence bounds gauge the extent to which one can rely on simulation results [START_REF] Chenney | Sampling plausible solutions to multi-body constraint problems[END_REF]. This uncertainty quantification mindset is pivotal, since robots will often times have limited knowledge about the environment they operate in and stimuli they are subject to.

Issue 6. Model calibration can be tedious. Populating a model with correct parameters is time consuming and most often an ad-hoc, one-off, process. Pragmatically speaking, the goal is to do predictive analysis, not to find parameters for a model. This observation explains why this topic receives less attention than the task of generating the model, which nonetheless is populated with these very parameters that ultimately dictate its quality. When experimental data for the robot is available, classical identification methods are effective provided the parameter space is not large [START_REF] Ljung | System identification: Theory for the user[END_REF]. Unfortunately, at design phase, robots exist only as CAD drafts yet to be instantiated in reality; as such, experimental data is unavailable.

Even when the robots exist, experimental data for calibration might be hard to procure. For instance, friction and contact simulation for rigid-wheel/deformable-terrain interaction requires more than 10 parameters, which are typically determined through a bevameter test carried out in the field [START_REF] Wong | Terramechanics and off-road vehicles[END_REF] 8). The hope is that using statistical learning techniques, one could bypass simulationspecific hurdles such as model generation and calibration by constructing "oracles" that are able to predict the next system state given its current one. The outcome of a data-driven approach might not be a complete elimination of the simulation. The approach could be used to methodically reduce model (and therefore computational) complexity through systematic dimensional reduction and/or model compression [START_REF] Farhat | Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency[END_REF][START_REF] Goury | Fast, generic, and reliable control and simulation of soft robots using model order reduction[END_REF]. However, the data-driven approaches in robotics simulation are in their infancy and principled methodologies to design and validate data-driven models remain to be identified. In addressing the issue at hand, one is faced with the practical challenge of developing realistic use cases and generating enough data to represent "truth" or "reality." This is not a trivial task. For instance, what types of data, problems, and level of authenticity are required to better capture and model the socio-physical characteristics of human-robot interaction scenarios? Note that in producing these surrogate models, one can substitute experimental data with simulator-generated data. Indeed, if a simulator and a model exist, the data pro-

D R A F T

duced by the simulator-model combo, just like experimental data, can be used to produce an "oracle" that within a controlled set of regimes can yield results close to those produced in simulation. In other words, the simulator provides the data that leads to its obsolescence. In a "domain randomization" approach, simple and therefore expeditious models are repeatedly used to canvas broad model parameter spaces via expeditious simulation to improve control policy robustness (5). Unfortunately, this example cannot be generalized and one should be cognizant of the fact that validated simulation tools serve multiple end goals -humanin-the-loop simulation, the designing of better control policies, improving mechanical performance, auditing for safety purposes, etc. This suggests that diversity in the level of model complexity is inevitable and the practitioner has to rely on past experience, understanding of the real world problem, and knowledge of the simulation tool to make the right call insofar as the needed level of complexity in the model is concerned.

Choosing a simple model for a complex problem might produce misleading results; conversely, choosing too complex of a model for a simple problem might take an unreasonable amount of effort to set up and lead to prohibitively long simulations times. These are two outcomes that each can discredit the idea of using simulation in robotics; they can also be used to make a case that simulation in robotics is part science and part practice.

Issue 9. Handling the human-robot interaction. One conspicuous challenge in HRI simulation is tied to the speed of execution: the robot simulation should be fast enough to support real-time interaction between the human and the robot model (human-in-the-loop simulation). This poses several challenges, see Issue 4. Speed of execution ceases to be a matter of concern if there is no actual human in the loop;

i.e., the human component is simulated as well. Indeed, in robot training, the human is replaced with an avatar endowed with the same traits that the human would display in his/her interaction with the robot. Creating avatars is as difficult as humans are diverse, each person a unique and complex web of intertwined physical, social, emotional, cognitive, and psychological threads. Numerous empirical models have been created to abstract human behavior in robotics and virtual reality [START_REF] Pantic | Human computing and machine understanding of human behavior: A survey in Artifical Intelligence for Human Computing[END_REF]. Ideally, the wisdom and insights that these studies have yielded would be aggregated in priors for mathematical models. This would mark a departure from today's empiricism 542 in human modeling in robotics and represent a step towards 543 the adoption of statistical models of the human in HRI tasks. 544 While progress has been reported in capturing mechanistic 545 traits in HRI, e.g., models for the mechanics of the human 546 body insofar as simulating surgery procedures is concerned 547 [START_REF] Courtecuisse | Real-time simulation of contact and cutting of heterogeneous soft-tissues[END_REF][START_REF] Albani | Virtual reality-assisted robotic surgery simulation[END_REF], numerous questions remain unanswered in relation to 548 abstracting in mathematical models the psychological under-549 pinnings that trigger in humans states of anxiety, fear, comfort, 550 stress, etc. In this context, the ability to control and display 551 emotions in avatars represents a prerequisite for endowing 552 smart robots with a sense of empathy in their interaction with 553 humans (50

D R A F T

multi-robot scenarios, one might need to simulate the communication layer. Any one of these sub-models is complex, both to generate and endow with meaningful parameters, see Issue 6. With an eye towards scenario composability, it would be useful and impactful to establish a set of freely available, readyto-go scenario models that build off existing robot/virtual world/sensor/humans/communication sub-models. Establishing model libraries is an arduous and long process that has one prerequisite: the community must coalesce around a modeling approach and ontology. The Unified Robot Description Format (URDF) (51) represents an early step in this direction, yet its modeling schema remains to be endowed with additional modeling elements. The existence of a rich and broadly adopted modeling language and model library will have the side benefit of providing an incentive for the manufacturers to augment these libraries with validated models associated with their robotics solutions. Finally, it is also imperative to identify new ways in which models can be validated. Fresh perspectives are needed in this regard, particularly when the robots simulated have not yet been realized [START_REF] Pepper | Robot simulation physics validation in Proceedings of the[END_REF]. This contribution outlines benefits that simulation can pro-811 vide in the smart robotics design process; and barriers that 812 need to be overcome for simulation to deliver on its poten-813 tial in this arena. Sharing the viewpoint that simulation is 814 not a goal in itself but a means to an end, we believe that 815 undertaking a focused, coordinated and sustained research 816 and software development effort in this area is timely. In 817 the process, the simulation community will have to address 818 research questions at the intersection of computer science, me-819 chanical engineering, electrical engineering, cognitive science, 820 and applied mathematics. Without a systematic, guided, and 821 all-encompassing effort, advancing the state of the art will 822 be marred by trial-and-error detours. The pressing question 823 is how to jump start a robotics-simulation cross-pollination 824 process that would speedily transition the effort from the "aca-825 demic debate" phase into a "simulation-enabled building and 826 demonstration of technology" phase. This transition can be 827 catalyzed by a sustained decade-long financial commitment, 828 which would ensure funding for cross-disciplinary efforts that 829 promote collaboration, competition and compilation of open 830 repositories of validated models and source code. As witnessed 831 in the aerospace and automotive industry, the paradigm shift 832 to digital, while manifestly impactful, took decades to coalesce. 833 Learning from this experience, the hope is that simulation 834 will lead to breakthroughs in the design of smart robots in a 835 matter of years rather than decades.

Fig. 1 .

 1 Fig. 1. Word cloud generated using participant-provided pre-workshop input. Word size proportional to use count; "simulation" was excluded.

Rec. 3 .

 3 Organize several simulation-in-robotics grand challenge competitions. Over the next decade, the community would benefit from a sequence of four or five grand challenge competitions. Run as a sequence, perhaps two years apart and akin to the 2013 DARPA's Virtual Robotics Challenge (53), these competitions could pay dividends in several ways. First, carefully chosen challenge themes would focus the community effort. Without direction and clear purpose, the community can choose to focus limited resources along technical thrusts of secondary relevance. Second, a set of wellchosen challenge themes would be a catalyst for cross-discipline collaboration. The current robotics simulation gaps are best addressed by multi-disciplinary teams, combining model developers and model users, particularly when the latter have expertise in physical testing. Third, such an effort will kindle and foster an ecosystem build-up effort to produce the methods, tools, models, and validation metrics of the trade. Fourth, this effort will likely produce a catalog of reliable simulation platforms available, as well as a community sanctioned set of recommendations that indicates which tool works well in what scenarios. Lastly, it is highly likely that these competitions will attract a lot public interest, which bodes well for promoting the field and getting the attention of tomorrow's roboticists. Rec. 4. Encourage efforts that seek to characterize the human-robot interaction. The issue of establishing human models that capture mechanical attributes of the body and/or psychological and cognitive traits of human behavior is cross-cutting. Applications in which HRI will come into play include robotic surgery, which is relevant in surgery training and remotely treating patients in remote/disaster zones; assisting seniors with tasks such as dressing, personal hygiene, cleaning and cooking; assisting individuals with limited ambulatory ability with transportation needs, etc. The body of work in the area of HRI modeling is meager, which explains the limited knowledge vis-à-vis the issue of human cognitive performance in HRI. In this context, there is a very limited set of science-based requirements and thresholds for safe human-robot interaction. Rec. 5. Encourage simulation solutions for soft robotics.

  the authors and do not necessarily reflect the views 846 of the U.S. Government and should not be construed as its of-847 ficial position, policy or decision unless so designated by other 848 documentation. No official endorsement is made or should be

225 Barriers to the use of simulation in robotics 226

	238	5. Uncertainty is marginally handled
	239	6. Model calibration can be tedious
	240	7. Data-driven approaches (surrogate models; replacing sim-
	241	ulation with an oracle) are only slowly emerging
	242	8. Difficulties in gauging the needed level of model complex-
	243	ity
	244	9. Handling the human-robot interaction
		Issue 1.
		R A F T
		D
		This section summarizes issues that, to various degrees, limit 227
		the role played by simulation in robotics. A noncomprehensive 228
		list of open problems vis-á-vis the topic at hand includes the 229
		following:	230
		1. Scarcity of solutions; producing a simulation platform for 231
		robotics requires broad multi-disciplinary expertise and 232
		sustained software development commitment	233
		2. Existing modeling languages are immature at a time when 234
		a robotics simulation ontology is only slowly emerging	235
		3. Model composability needs further improvements	236
		4. Simulation is not fast enough

Scarcity of solutions; producing a simu- 245 lation platform for robotics requires broad multi- 246 disciplinary expertise and sustained software devel-

	247
	248	opment commitment. After an evolution that spanned
	249	decades, Computer Aided Engineering (CAE); i.e., the use of
	250	computer simulation in customary engineering, has become
	251	a linchpin in the design process. For instance, one can carry
	252	out a highly predictive finite element analysis of an engine
	253	crank shaft or of an airplane wing to accurately identify vi-
	254	bration/flutter modes. Likewise, a vehicle ride analysis can
	255	be performed via a dynamics simulation in which a vehi-
	256	cle operating on a flat, rigid surface follows a sequence of
	257	driving maneuvers to collect information for a subsequent
	258	noise/vibration/harshness analysis. Compared to what a CAE
	259	simulation looks like today, simulation in robotics is less struc-
	260	tured and vastly more multi-disciplinary. The "less structured"
	261	attribute is tied to the observation that a relatively simple
	262	set of boundary conditions and load functions are enough to
	263	define the backdrop for a CAE-type analysis. In robotics, the
	264	backdrop is more complex and uncertain, and thus harder to
	265	define. For instance, the robot can operate in and interact
	266	with an unstructured and evolving world; or a human can be
	267	part of the equation. The "multi-disciplinary" attribute is
	268	a consequence of the fact that for simulation in robotics, in
	269	addition to producing models for robot dynamics, which is in
	270	the purview of CAE, one has to engage in non-CAE tasks, e.g.,
		bring in sensor simulation required for agent control; represent

271 in high fidelity the unstructured environment surrounding the 272 robot (this virtual world might have to be itself simulated 273 since its state can be influenced by the robot dynamics, e.g., 274 rover changing the world by creating deep ruts that must be 275 captured by the camera sensor of a following rover); simulate 276 the human dimension in HRI; and, simulate agent-to-agent or 277 agent-to-infrastructure communication. These simulator sub-278 components, which are tied to the dynamics, sensing, virtual 279 world, human participation, and communication facets render 280 the simulation-in-robotics task highly multi-disciplinary. As 281 an example, consider sensor simulation -it alone calls for 282 knowledge in several disciplines as robotics applications rely 283 on multiple classes of sensors, e.g., GPS, IMU, LiDAR, camera, 284 radar, thermal, etc., each drawing on vastly different physics 285 for characterizing the sensing paradigms that come into play.

286

Finally, one has to integrate these subcomponents and turn the 287 sum of the parts into an usable simulation platform, a process 288 that adds a software engineering facet to the multi-disciplinary 289 nature of the task at hand. 290 Issue 2. Existing modeling languages are immature at 291 a time when a robotics simulation ontology is only 292 slowly emerging. Specifying/defining/setting up a scenario 293 model for simulation in robotics can be daunting. Indeed, one 294 has to specify a slew of parameters that define the geometry 295 of the robot, its kinematics, loads, actuators, sensor package 296 information, the virtual world it operates in (weather, time of 297

driven approaches (surrogate models; replacing simulation with an oracle) are only slowly emerging

. Although the rover might exist, experimental data for Martian soil is very limited and a bevameter test is out of question. Recent efforts aimed at simplifying calibration rely on Machine Learning and other statistical approaches to employ iterative processes that automate what remains a labor and time intensive process (6, 42, 43). For large parameter sets, brute force approaches can leverage powerful processors to perform statistically guided extensive searches for parameter values, see, for instance,

[START_REF] Collins | Traversing the reality gap via simulator tuning[END_REF]

.

Issue 7. Data-. The standardization of robotic platforms open the possibility to collect large amounts of data (sometimes provided by the manufacturer) in real world scenarios that could be leveraged by Machine Learning techniques to build predictive models directly from data (

steps for making simulation more useful in 555 robotics 556

  The next two subsections summarize, respectively, recommen-557 dations and high-vantage-point observations that emerged at 558 the conclusion of the simulation-in-robotics workshop. The recommenda-560 tions below are made for a generic agency that considers 561 making investments in the simulation-in-robotics area while 562 the agency is at the stage of assembling a call for proposals. 563 The recommendations could also be informative to someone 564 who contemplates becoming involved in the research and de-565 velopment effort associated with the simulation-in-robotics 566 topic. The list is not comprehensive; it only compiles several 567 initiatives deemed as of higher priority.

		568
	1. Foster the development and validation of open source 569
	simulation platforms	570
	2. Foster efforts that seek to establish open and community 571
	curated libraries of validated models	572
	3. Organize several simulation-in-robotics grand challenge 573
	competitions	574
	4. Encourage efforts that seek to characterize the human-575
	robot interaction	576
	5. Encourage simulation solutions for soft robotics	577
	6. Emphasize use of emerging hardware architectures

). 554 Suggested 559 A. "Nuts-and-bolts" recommendations. 578 Rec. 1.

Foster the development and validation of open 579 source simulation platforms. A

  robust and feature-rich 580 set of four or five simulation tools available in the open-581 source domain is critical to advancing the state of the art 582 in robotics. Validated open source platforms democratize 583 the simulation-in-robotics effort and inspire/inform future, 584 more refined open-source or commercial efforts. Trusted open 585source solutions are quickly embraced; support the idea of 586 reproducibility/verifiability in science; and have the side effect 587 of immediately raising the bar for the commercial tools, which 588 must necessarily up the ante. Owing to the breadth of robotics 589 applications, it is likely that no single platform will emerge 590 as the solution of choice for all targeted simulation scenarios, 591 which provides the rationale for the "four or five simulation 592 platforms" recommendation above.

593

Rec. 2. Foster efforts that seek to establish open and 594 community curated libraries of validated models. Cus-595 tomarily, simulation in robotics calls for the interplay of three 596 types of sub-models: robots, synthetic worlds, and sensors. In 597 some cases, the human component comes into play, and, for 598

Building a simulation-in-robotics ecosys-695 tem requires a sustained and multi-disciplinary effort 696 that poses numerous open questions in basic research. 697

  To date, robotics simulation has almost exclu-sively drawn on rigid body dynamics. Indeed, the underlying 659 modeling is simpler, the software implementation effort is more 660 reasonable, and the simulation run-times are shorter. Looking 661 ahead, support for soft robotics is critical in several fields, e.g., 662 HRI and biomimetic robots. It is anticipated that embracing 663 compliance in the robotics models will elicit new approaches to 664 handling frictional contact with the potential benefit of allevi-665 ating numerical artifacts/paradoxes brought to the fore by the 666 rigid body model[START_REF] Stewart | Rigid-body dynamics with friction and impact[END_REF]. Generating through simulation sensory-667 motor data that matches the multi-resolution dynamics, noise, 668 softness, etc. of sensors and actuators during complex tasks 669 that include both compliance and frictional contacts is poised 670 to open the door to a systematic study of the sensory-motor 671 space for robotic manipulation and locomotion. Recent advances in high-bandwidth memory and large 674 core/processor counts can partially alleviate increased compu-675 tational loads associated with terramechanics, soft robotics, 676 fluid-solid interaction and real-time simulation. Looking be-677 yond GPU and multi-core computing, a federated approach 678[START_REF] Enge-Rosenblatt | Functional digital mock-up and the functional mock-up interface-two complementary approaches for a comprehensive investigation of heterogeneous systems[END_REF] in which various components of a model are handled sepa-679 rately by different solvers in a co-simulation framework would: 680 facilitate a plug-and-play vision that enables scalability of the 681 solution; open the door to contributions coming from multiple 682 groups; and enable the adoption of best-in-class solvers for 683 subproblems. Unlike the previous subsec-685 tion, which provided pointed recommendations, the focus 686 below is on capstone observations. Several types of simulation techniques have to be combined 698 to produce a simulation-in-robotics solution platform. A dy-699 namics engine is needed to capture the time evolution of the 700 robots in environments that can be unstructured and them-701 selves be changing in time; sensor simulation is required in 702 controlling the agents; meaningful sensor simulation is predi-703 cated on the ability to simulate the environment and model 704 its unstructured and time-dependent nature; simulating the 705 human dimension in HRI; finally, communication simulation 706 comes into play for agent-to-agent or agent-to-infrastructure 707 communication. Open problems that defy our current level of 708 understanding riddle each of these simulation aspects whose in-709 terplay ultimately defines the concept of simulation in robotics.710 Addressing these open problems requires basic research in a 711 variety of areas: physics-based modeling (friction, contact, 712 impact, soft/compliant bodies, fluid-solid interaction, terrame-713 chanics); numerical methods (fast real-time solvers, linear 714 solvers, optimization techniques, computational geometry, the 715

	numerical solution of differential algebraic and partial differ-
	ential equations, nonlinear finite element analysis, computa-
	tional fluid dynamics, etc.); software development (hardware-
	aware software); and data analysis (uncertainty quantification).
	Serendipitously, several funded initiatives are ongoing, e.g.,
	soft robotics (NSF) and terramechanics (DOD), and bound
	to have an impact for the problem at hand. Nonetheless, the
	majority of the simulation-in-robotics multi-disciplinary open
	issues, e.g., modeling the human component in HRI, sensor
	modeling, model composability, etc., linger and at best are
	tangentially addressed in a context that lacks synergy and
	is non-programmatic. Against this backdrop, we are of the
	opinion that the benefits of a broad multi-agency initiative
	could be twofold: stimulate fundamental research in relevant	672
	areas, and foster its rapid translation into open source sim-
	ulation platforms. A broad initiative of this caliber would	Rec. 6. Emphasize use of emerging hardware architec-673
	1. Building a simulation-in-robotics ecosystem requires a sus-688 tained and multi-disciplinary effort that poses numerous 689 open questions in basic research 690 2. Simulation-in-robotics has a foundational and sizable soft-691 ware development component to it 692 has manifest societal impact 694 High Level 1. D 3. Simulation-in-robotics provides a grand challenge that 693 R A F T bring together, ideally in an international framework, research groups from academia and agile software development groups from academia, research labs, and industry. tures. 687 High Level 2. Simulation-

684

B. "High-vantage-point" aspects.

in-robotics has a foundational and sizable software development component to it.

  It is this group's belief that computer simulation can and should play 777 an important role in smart robotics. We take our cue from 778 the enthusiastic adoption and broad impact that computer 779 simulation has had in other industries and endeavors -from 780 building cars and airplanes to planetary exploration. Yet 781 compared to CAD, computer aided manufacturing (CAM) 782 and CAE solutions that anchor both the product life-cycle 783 in industry and a vigorous research enterprise in academia, 784 the simulation in robotics field is in its infancy. Moreover, 785 beyond lack of maturity, simulation in robotics is faced with 786 the task of serving user groups pulling in different directions 787 by virtue of them being engaged in different activities such 788 as design (will it break? is it fast enough? can we build it?), 789 controls (how can we make it reconfigure? how does it climb 790 stairs? how does it work with other robots? will it work 791 under limited sensing?), machine learning (learn gradually 792 without forgetting; how do I close the sim-to-real gap?) and 793 artificial intelligence (when and how to express empathy?). 794 Lastly, by comparison with CAD/CAM/CAE, simulation in 795 robotics poses unique and truly multi-disciplinary challenges, 796 some of them summarized in this document. While estab-797 lished commercial CAD/CAM/CAE solution providers, e.g., 798 MSC.ADAMS, Siemens, Dassault Systèms, FunctionBae, etc., 799 are anticipated to gradually address the needs of the robotics 800 community, our near-term hopes are pinned on nimbler and 801 more focused platforms, e.g., Bullet[START_REF] Coumans | Pybullet, a python module for physics simulation for games[END_REF], Chrono[START_REF]Chrono: An Open Source Framework for the Physics-Based Simulation of Dynamic Systems[END_REF], DART 802[START_REF] Lee | DART: Dynamic animation and robotics toolkit[END_REF], Drake[START_REF] Tedrake | Drake: A planning, control, and analysis toolbox for nonlinear dynamical systems[END_REF], MuJoCo[START_REF] Todorov | Mujoco: A physics engine for model-based control in[END_REF], ODE[START_REF] Smith | Open Dynamics Engine[END_REF], SOFA[START_REF] Faure | Sofa: A multi-model framework for interactive physical simulation in Soft tissue biomechanical modeling for computer assisted surgery[END_REF]. These 803 platforms are by and large open source; are plugged deeper into 804 the research community; and have the flexibility to rapidly 805 translate modeling, numerical methods, sensor simulation, 806 graphics, and emerging hardware architectures breakthroughs 807 into advances that amplify the impact that simulation plays 808 in robotics.

	While posing significant challenges, software development is
	not a research activity insofar as simulation-in-robotics is con-
	cerned. Yet the process adopted for developing the necessary
	software infrastructure along with the terms under which the
	tiative open source released under a permissive license such as
	MIT is more attractive only for the reason that a component of
	it, be it a graphical user interface, data input/output facilities,
	a particular algorithm, a collision detection implementation,
	etc., might be recycled by another effort that itself might be
	open or closed source. In our experience, the argument that
	an open source code grows faster owing to contributions from
	volunteers has proven largely unsubstantial. Indeed, the level
	of skill and expertise required to make meaningful contribu-
	tions to a project are prohibitive, particularly early on in the
	trail-blazing phase of the project. In fact, the point in time
	in the software life cycle when volunteers can help marks the
	moment when the project would ideally go commercial or be
	sponsored by such an entity. This brings up the question of
	who should develop the software that enables the simulation-
	in-robotics vision. Encouraging domain experts to engage in
	software development pursuits that have a manifest transla-
	tion attribute; i.e., demonstrating new algorithms, modeling
	approaches, etc., would be beneficial, particularly at the onset
	of the initiative and when done with a mindset of generating
	open-source software. Ideally, these domain-expert generated
	software components would be recycled by more mature sim-

software is released play a role in how soon simulation makes a difference in robotics. An important aspect is whether or not the software that underlies a simulation-in-robotics initiative should be released as open source. Likewise, there are several licenses, some more permissive than others, vis-à-vis how opensource can be used/modified/distributed. The salient point is that software that solves the problem at hand is desirable in any form. Perhaps, at the onset of a simulation-in-robotics iniulation platforms. In this ecosystem, as the field matures, one can hope that bigger and perhaps commercial entities would step in to carry the burden of adding the features of convenience that accelerate solution adoption. High Level 3. Simulation-in-robotics provides a grand challenge that has manifest societal impact. 809 Conclusions 810

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Acknowledgments and disclaimers

of training data for Machine Learning nology, which hosted the workshop event. We grateful for the input provided by more than other 30 experts who attended 843 the event but are not listed as co-authors on this document.

844

The views, opinions and/or findings contained in this work