Learning to Brachiate via Simplified Model Imitation

TL;DR

This paper introduces a reinforcement learning approach for brachiation using a simplified model to guide policy learning and planning, enabling a finger-less planar model to learn complex swinging motions across challenging handhold sequences.

Contribution

It presents a novel simplified model imitation method that facilitates learning and planning of brachiation in complex environments, advancing robotic locomotion techniques.

Findings

Successfully learned brachiation motions with variable durations.

Emergence of additional swings for efficiency.

Guided policy learning improves performance.

Abstract

Brachiation is the primary form of locomotion for gibbons and siamangs, in which these primates swing from tree limb to tree limb using only their arms. It is challenging to control because of the limited control authority, the required advance planning, and the precision of the required grasps. We present a novel approach to this problem using reinforcement learning, and as demonstrated on a finger-less 14-link planar model that learns to brachiate across challenging handhold sequences. Key to our method is the use of a simplified model, a point mass with a virtual arm, for which we first learn a policy that can brachiate across handhold sequences with a prescribed order. This facilitates the learning of the policy for the full model, for which it provides guidance by providing an overall center-of-mass trajectory to imitate, as well as for the timing of the holds. Lastly, the simplified model can also readily be used for planning suitable sequences of handholds in a given environment. Our results demonstrate brachiation motions with a variety of durations for the flight and hold phases, as well as emergent extra back-and-forth swings when this proves useful. The system is evaluated with a variety of ablations. The method enables future work towards more general 3D brachiation, as well as using simplified model imitation in other settings.