Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization
This addresses fall prevention for humanoid robots, which is an incremental improvement in robotics planning.
The paper tackles the problem of humanoid robot fall mitigation by developing a unified planner that optimizes contact sequences and robot trajectories through contact transition tree optimization, demonstrating the algorithm can generate complex stabilization strategies for simulated robots under varying pushes and environments.
This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement) to eliminate kinetic energy. This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization. Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated robot under varying initial pushes and environment shapes.