Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning

TL;DR

This paper introduces a method using a simplified single rigid-body model to generate reference trajectories for dynamic bipedal robot maneuvers, enabling effective transfer from simulation to real hardware through reinforcement learning.

Contribution

The authors develop a framework that leverages a single rigid-body model to optimize trajectories for dynamic bipedal motions and transfer them to real robots using reinforcement learning.

Findings

Successfully transferred dynamic gaits to real hardware

Achieved running speeds up to 3.0 m/s on Cassie

Demonstrated robustness of learned controllers in real-world scenarios

Abstract

In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning. Our approach is to utilize a single rigid-body model (SRBM) to optimize libraries of trajectories offline to be used as expert references in the reward function of a learned policy. This method translates the model's dynamically rich rotational and translational behaviour to a full-order robot model and successfully transfers to real hardware. The SRBM's simplicity allows for fast iteration and refinement of behaviors, while the robustness of learning-based controllers allows for highly dynamic motions to be transferred to hardware. % Within this work we introduce a set of transferability constraints that amend the SRBM dynamics to actual bipedal robot hardware, our framework for creating optimal trajectories for dynamic stepping, turning maneuvers and jumps as well as our approach to integrating reference trajectories to a reinforcement learning policy. Within this work we introduce a set of transferability constraints that amend the SRBM dynamics to actual bipedal robot hardware, our framework for creating optimal trajectories for a variety of highly dynamic maneuvers as well as our approach to integrating reference trajectories for a high-speed running reinforcement learning policy. We validate our methods on the bipedal robot Cassie on which we were successfully able to demonstrate highly dynamic grounded running gaits up to 3.0 m/s.