A class of smoothing functions for nonlinear and mixed complementarity problems
Computational Optimization and Applications
Computational Optimization and Applications
Rigid-Body Dynamics with Friction and Impact
SIAM Review
Practical methods for optimal control using nonlinear programming
Practical methods for optimal control using nonlinear programming
Local Convergence of SQP Methods for Mathematical Programs with Equilibrium Constraints
SIAM Journal on Optimization
Optimal gait and form for animal locomotion
ACM SIGGRAPH 2009 papers
Bounding on rough terrain with the LittleDog robot
International Journal of Robotics Research
Discovery of complex behaviors through contact-invariant optimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Contact-invariant optimization for hand manipulation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
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Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning of rigid-body systems that contact their environment through inelastic impacts and Coulomb friction. This method eliminates the requirement for a priori mode ordering. Motivated by the formulation of multi-contact dynamics as a Linear Complementarity Problem for forward simulation, the proposed algorithm poses the optimization problem as a Mathematical Program with Complementarity Constraints. We leverage Sequential Quadratic Programming to naturally resolve contact constraint forces while simultaneously optimizing a trajectory that satisfies the complementarity constraints. The method scales well to high-dimensional systems with large numbers of possible modes. We demonstrate the approach on four increasingly complex systems: rotating a pinned object with a finger, simple grasping and manipulation, planar walking with the Spring Flamingo robot, and high-speed bipedal running on the FastRunner platform.