Efficient Search and Learning for Agile Locomotion on Stepping Stones
arxiv(2024)
摘要
Legged robots have become capable of performing highly dynamic maneuvers in
the past few years. However, agile locomotion in highly constrained
environments such as stepping stones is still a challenge. In this paper, we
propose a combination of model-based control, search, and learning to design
efficient control policies for agile locomotion on stepping stones. In our
framework, we use nonlinear model predictive control (NMPC) to generate
whole-body motions for a given contact plan. To efficiently search for an
optimal contact plan, we propose to use Monte Carlo tree search (MCTS). While
the combination of MCTS and NMPC can quickly find a feasible plan for a given
environment (a few seconds), it is not yet suitable to be used as a reactive
policy. Hence, we generate a dataset for optimal goal-conditioned policy for a
given scene and learn it through supervised learning. In particular, we
leverage the power of diffusion models in handling multi-modality in the
dataset. We test our proposed framework on a scenario where our quadruped robot
Solo12 successfully jumps to different goals in a highly constrained
environment.
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