Linear Policies are Sufficient to Realize Robust Bipedal Walking on Challenging Terrains

TL;DR

This paper demonstrates that simple linear policies can enable a bipedal robot to walk robustly on challenging terrains, achieving high performance with fewer parameters and easier interpretability than neural network approaches.

Contribution

The authors introduce a control pipeline using linear policies for high-level trajectory modulation and low-level gait regulation, enabling robust and sample-efficient bipedal walking on uneven terrains.

Findings

Successful transfer from simulation to real hardware

Robust walking on slopes, stairs, and outdoor terrains

Linear policies outperform complex neural network policies in efficiency

Abstract

In this work, we demonstrate robust walking in the bipedal robot Digit on uneven terrains by just learning a single linear policy. In particular, we propose a new control pipeline, wherein the high-level trajectory modulator shapes the end-foot ellipsoidal trajectories, and the low-level gait controller regulates the torso and ankle orientation. The foot-trajectory modulator uses a linear policy and the regulator uses a linear PD control law. As opposed to neural network-based policies, the proposed linear policy has only 13 learnable parameters, thereby not only guaranteeing sample efficient learning but also enabling simplicity and interpretability of the policy. This is achieved with no loss of performance on challenging terrains like slopes, stairs and outdoor landscapes. We first demonstrate robust walking in the custom simulation environment, MuJoCo, and then directly transfer to hardware with no modification of the control pipeline. We subject the biped to a series of pushes and terrain height changes, both indoors and outdoors, thereby validating the presented work.