Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo

We describe a full-featured simulation pipeline implemented in the MuJoCo physics engine. It includes multi-joint dynamics in generalized coordinates, holonomic constraints, dry joint friction, joint and tendon limits, frictionless and frictional contacts that can have sliding, torsional and rolling friction. The forward dynamics of a 27-dof humanoid with 10 contacts are evaluated in 0.1 msec. Since the simulation is stable at 10 msec timesteps, it can run 100 times faster than real-time on a single core of a desktop processor. Furthermore the entire simulation pipeline can be inverted analytically, an order-of-magnitude faster than the corresponding forward dynamics. We soften all constraints, in a way that avoids instabilities and unrealistic penetrations associated with earlier spring-damper methods and yet is sufficient to allow inversion. Constraints are imposed via impulses, using an extended version of the velocity-stepping approach. For holomonic constraints the extension involves a soft version of the Gauss principle. For all other constraints we extend our earlier work on complementarity-free contact dynamics - which were already known to be invertible via an iterative solver - and develop a new formulation allowing analytical inversion.

[1]  R. Kalaba,et al.  A new perspective on constrained motion , 1992, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[2]  J. Baumgarte Stabilization of constraints and integrals of motion in dynamical systems , 1972 .

[3]  Dylan A. Shell,et al.  Modeling Contact Friction and Joint Friction in Dynamic Robotic Simulation Using the Principle of Maximum Dissipation , 2010, WAFR.

[4]  D. Stewart,et al.  Time-stepping for three-dimensional rigid body dynamics , 1999 .

[5]  Emanuel Todorov,et al.  A convex, smooth and invertible contact model for trajectory optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Yuval Tassa,et al.  Physically-consistent sensor fusion in contact-rich behaviors , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Roy Featherstone,et al.  Efficient Factorization of the Joint-Space Inertia Matrix for Branched Kinematic Trees , 2005, Int. J. Robotics Res..

[8]  A. Ruina,et al.  A New Algebraic Rigid-Body Collision Law Based on Impulse Space Considerations , 1998 .

[9]  D. Stewart,et al.  AN IMPLICIT TIME-STEPPING SCHEME FOR RIGID BODY DYNAMICS WITH INELASTIC COLLISIONS AND COULOMB FRICTION , 1996 .

[10]  Zoran Popovic,et al.  Discovery of complex behaviors through contact-invariant optimization , 2012, ACM Trans. Graph..

[11]  Roy Featherstone,et al.  Rigid Body Dynamics Algorithms , 2007 .

[12]  Yuval Tassa,et al.  Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Dinesh K. Pai,et al.  Staggered projections for frictional contact in multibody systems , 2008, SIGGRAPH Asia '08.

[14]  Dylan A. Shell,et al.  An evaluation of methods for modeling contact in multibody simulation , 2011, 2011 IEEE International Conference on Robotics and Automation.