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Robotics and Autonomous Systems

Simulation is a core component of robotics workflows that can shed light on the complex interplay between a physical body, the environment and sensory feedback mechanisms in silico. To this goal several simulation methods, originating in rigid body dynamics and in continuum mechanics have been employed, enabling the simulation of a plethora of phenomena such as rigid/soft body dynamics, fluid dynamics, muscle simulation as well as sensor and actuator dynamics. The physics engines commonly employed in robotics simulation focus on rigid body dynamics, whereas continuum mechanics methods excel on the simulation of phenomena where deformation plays a crucial role, keeping the two fields relatively separate. Here, we propose a shift of paradigm that allows for the accurate simulation of fluids in interaction with rigid bodies within the same robotics simulation framework, based on the continuum mechanics-based Smoothed Particle Hydrodynamics method. The proposed framework is useful for simulations such as swimming robots with complex geometries, robots manipulating fluids and even robots emitting highly viscous materials such as the ones used for 3D printing. Scenarios like swimming on the surface, air-water transitions, locomotion on granular media can be natively simulated within the proposed framework. Firstly, we present the overall architecture of our framework and give examples of a concrete software implementation. We then verify our approach by presenting one of the first of its kind simulation of self-propelled swimming robots with a smooth particle hydrodynamics method and compare our simulations with real experiments. Finally, we propose a new category of simulations that would benefit from this approach and discuss ways that the sim-to-real gap could be further reduced.

2024 IEEE International Conference on Robotics and Automation (ICRA)

The use of simulation in robotics is increasingly widespread for the purpose of testing, synthetic data generation and skill learning. A relevant aspect of simulation for a variety of robot applications is physics-based simulation of robot-object interactions. This involves the challenge of accurately modeling and implementing different mechanical systems such as rigid and deformable bodies as well as their interactions via constraints, contact or friction. Most state-of-the-art physics engines commonly used in robotics either cannot couple deformable and rigid bodies in the same framework, lack important systems such as cloth or shells, have stability issues in complex friction-dominated setups or cannot robustly prevent penetrations. In this paper, we propose a framework for strongly coupled simulation of rigid and deformable bodies with focus on usability, stability, robustness and easy access to state-of-the-art deformation and frictional contact models. Our system uses the Finite Element Method (FEM) to model deformable solids, the Incremental Potential Contact (IPC) approach for frictional contact and a robust second order optimizer to ensure stable and penetration-free solutions to tight tolerances. It is a general purpose framework, not tied to a particular use case such as grasping or learning, it is written in C++ and comes with a Python interface. We demonstrate our system’s ability to reproduce complex real-world experiments where a mobile vacuum robot interacts with a towel on different floor types and towel geometries. Our system is able to reproduce 100% of the qualitative outcomes observed in the laboratory environment. The simulation pipeline, named Stark (the German word for strong, as in strong coupling) is made open-source.

IEEE Transactions on Visualization and Computer Graphics

Dynamics simulation with frictional contacts is important for a wide range of applications, from cloth simulation to object manipulation. Recent methods using smoothed lagged friction forces have enabled robust and differentiable simulation of elastodynamics with friction. However, the resulting frictional behavior can be inaccurate and may not converge to analytic solutions. Here we evaluate the accuracy of lagged friction models in comparison with implicit frictional contact systems. We show that major inaccuracies near the stick-slip threshold in such systems are caused by lagging of friction forces rather than by smoothing the Coulomb friction curve. Furthermore, we demonstrate how systems involving implicit or lagged friction can be correctly used with higher-order time integration and highlight limitations in earlier attempts. We demonstrate how to exploit forward-mode automatic differentiation to simplify and, in some cases, improve the performance of the inexact Newton method. Finally, we show that other complex phenomena can also be simulated effectively while maintaining smoothness of the entire system. We extend our method to exhibit stick-slip frictional behavior and preserve volume on compressible and nearly-incompressible media using soft constraints.