Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning
arXiv:2408.07508 · doi:10.1109/LRA.2024.3519908
Abstract
Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work, we present a novel approach that optimizes gait sequences and respective timings for legged robots in the context of optimization-based controllers through the use of sampling-based methods and supervised learning techniques. We propose to bootstrap the search by learning an optimal value function in order to speed-up the gait planning procedure making it applicable in real-time. To validate our proposed method, we showcase its performance both in simulation and on hardware using a 22 kg electric quadruped robot. The method is assessed on different terrains, under external perturbations, and in comparison to a standard control approach where the gait sequence is fixed a priori.
References in corpus (8)
- Learning agile and dynamic motor skills for legged robots
- Learning robust perceptive locomotion for quadrupedal robots in the wild
- Efficient Multi-Contact Pattern Generation with Sequential Convex Approximations of the Centroidal Dynamics
- Accelerating Model Predictive Control for Legged Robots through Distributed Optimization
- On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion
- Perceptive Locomotion through Whole-Body MPC and Optimal Region Selection
- SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
- ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion