Data-Augmented Contact Model for Rigid Body Simulation

Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand challenge for existing rigid-body physics simulators. This paper introduces a data-augmented contact model that incorporates analytical solutions with observed data to predict the 3D contact impulse which could result in rigid bodies bouncing, sliding or spinning in all directions. Our method enhances the expressiveness of the standard Coulomb contact model by learning the contact behaviors from the observed data, while preserving the fundamental contact constraints whenever possible. For example, a classifier is trained to approximate the transitions between static and dynamic frictions, while non-penetration constraint during collision is enforced analytically. Our method computes the aggregated effect of contact for the entire rigid body, instead of predicting the contact force for each contact point individually, removing the exponential decline in accuracy as the number of contact points increases.

[1]  J. T. Oden,et al.  Models and computational methods for dynamic friction phenomena , 1984 .

[2]  J. Kalker,et al.  On the rolling contact of two elastic bodies in the presence of dry friction , 1967 .

[3]  Cheng Yang,et al.  Data‐driven projection method in fluid simulation , 2016, Comput. Animat. Virtual Worlds.

[4]  Carlos Canudas de Wit,et al.  Friction Models and Friction Compensation , 1998, Eur. J. Control.

[5]  Barbara Solenthaler,et al.  Data-driven fluid simulations using regression forests , 2015, ACM Trans. Graph..

[6]  Kuan-Ting Yu,et al.  More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Nima Fazeli,et al.  Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact , 2017, CoRL.

[8]  Brian Mirtich,et al.  Impulse-based dynamic simulation of rigid body systems , 1996 .

[9]  Valentin L. Popov,et al.  The research works of Coulomb and Amontons and generalized laws of friction , 2015 .

[10]  Dieter Fox,et al.  SE3-nets: Learning rigid body motion using deep neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Marcin Andrychowicz,et al.  Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Javier Ruiz-del-Solar,et al.  Back to reality: Crossing the reality gap in evolutionary robotics , 2004 .

[13]  David Baraff,et al.  Analytical methods for dynamic simulation of non-penetrating rigid bodies , 1989, SIGGRAPH.

[14]  Maria Bauza,et al.  A probabilistic data-driven model for planar pushing , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Jeffrey C. Trinkle,et al.  An implicit time-stepping scheme for rigid body dynamics with Coulomb friction , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[16]  Rob Fergus,et al.  Learning Physical Intuition of Block Towers by Example , 2016, ICML.

[17]  Wojciech Zaremba,et al.  Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Shu-Wei Hsu,et al.  Statistical simulation of rigid bodies , 2009, SCA '09.

[19]  David Baraff,et al.  Dynamic Simulation of Non-penetrating Rigid Bodies , 1992 .

[20]  Ken Perlin,et al.  Accelerating Eulerian Fluid Simulation With Convolutional Networks , 2016, ICML.

[21]  Geoffrey E. Hinton,et al.  NeuroAnimator: fast neural network emulation and control of physics-based models , 1998, SIGGRAPH.

[22]  Leslie Pack Kaelbling,et al.  Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[23]  Greg Turk,et al.  Preparing for the Unknown: Learning a Universal Policy with Online System Identification , 2017, Robotics: Science and Systems.

[24]  Joshua B. Tenenbaum,et al.  A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.

[25]  G. Dantzig,et al.  COMPLEMENTARY PIVOT THEORY OF MATHEMATICAL PROGRAMMING , 1968 .

[26]  Karen Liu.  Dynamic Animation and Robotics Toolkit , 2014 .

[27]  Siddhartha S. Srinivasa,et al.  DART: Dynamic Animation and Robotics Toolkit , 2018, J. Open Source Softw..

[28]  Emanuel Todorov,et al.  Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[29]  H. Kuk.  On equilibrium points in bimatrix games , 1996 .

[30]  Andy Ruina,et al.  Limit surface and moment function descriptions of planar sliding , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[31]  Klaus Knothe,et al.  History of wheel/rail contact mechanics: from Redtenbacher to Kalker , 2008 .

[32]  F. Jourdan,et al.  A Gauss-Seidel like algorithm to solve frictional contact problems , 1998 .

[33]  M. Anitescu,et al.  A Time-stepping Method for Stii Multibody Dynamics with Contact and Friction ‡ , 2022 .

[34]  J. Andrew Bagnell,et al.  A convex polynomial force-motion model for planar sliding: Identification and application , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[35]  Stefan Schaal,et al.  Combining learned and analytical models for predicting action effects from sensory data , 2017, Int. J. Robotics Res..