Efficient Multi-Contact Pattern Generation with Sequential Convex Approximations of the Centroidal Dynamics

Abstract

This paper investigates the problem of efficient computation of physically consistent multi-contact behaviors. Recent work showed that under mild assumptions, the problem could be decomposed into simpler kinematic and centroidal dynamic optimization problems. Based on this approach, we propose a general convex relaxation of the centroidal dynamics leading to two computationally efficient algorithms based on iterative resolutions of second order cone programs. They optimize centroidal trajectories, contact forces and, importantly, the timing of the motions. We include the approach in a kino-dynamic optimization method to generate full-body movements. Finally, the approach is embedded in a mixed-integer solver to further find dynamically consistent contact sequences. Extensive numerical experiments demonstrate the computational efficiency of the approach, suggesting that it could be used in a fast receding horizon control loop. Executions of the planned motions on simulated humanoids and quadrupeds and on a real quadruped robot further show the quality of the optimized motions.

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Cited by in corpus (8)

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• ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots
• Adaptive Complexity Model Predictive Control
• Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning
• ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion
• Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations
• Task and Motion Planning for Humanoid Loco-manipulation
• Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning