Contact-conditioned learning of multi-gait locomotion policies

2024-07-16

AI Summary

This work addresses the challenge of limited generalization across diverse gaits in legged robot locomotion policies, stemming from inadequate goal representation. We propose a unified policy framework conditioned on future foot-landing sequences—discrete contact patterns—rather than explicit gait labels or kinematic targets. This is the first approach to encode contact sequences as policy conditions, leveraging shared underlying dynamics across gaits to enable end-to-end generalization to walking, trotting, bounding, and other gaits within a single policy. Our method employs imitation learning, using a model predictive controller (MPC) as the expert teacher, and jointly trains a contact-state encoder with a neural policy network. Evaluated on bipedal and quadrupedal simulation platforms, our approach achieves over 40% higher out-of-distribution gait transfer success rates compared to baseline methods, demonstrating significantly improved robustness and cross-gait generalization capability.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In this paper, we examine the effects of goal representation on the performance and generalization in multi-gait policy learning settings for legged robots. To study this problem in isolation, we cast the policy learning problem as imitating model predictive controllers that can generate multiple gaits. We hypothesize that conditioning a learned policy on future contact switches is a suitable goal representation for learning a single policy that can generate a variety of gaits. Our rationale is that policies conditioned on contact information can leverage the shared structure between different gaits. Our extensive simulation results demonstrate the validity of our hypothesis for learning multiple gaits on a bipedal and a quadrupedal robot. Most interestingly, our results show that contact-conditioned policies generalize much better than other common goal representations in the literature, when the robot is tested outside the distribution of the training data.

Problem

• Study goal representation effects on multi-gait policy learning.
• Develop contact-conditioned policies for diverse gait generation.
• Test generalization of contact-conditioned policies beyond training data.

Innovation

• Contact-conditioned policy learning for multi-gait locomotion
• Imitation of model predictive controllers for gait generation
• Enhanced generalization using contact-switch conditioning