Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design

TL;DR

This paper introduces a method that uses reachability analysis on simplified models to generate safety-preserving controllers for complex bipedal robots, enabling safe online walking control.

Contribution

It presents a novel approach combining reachability analysis with MPC to ensure safety in online gait control for high-dimensional robots.

Findings

Successfully applied to a 5-link RABBIT model

Enables safe online gait control using simplified models

Provides formal safety guarantees through reachability analysis

Abstract

Online control design using a high-fidelity, full-order model for a bipedal robot can be challenging due to the size of the state space of the model. A commonly adopted solution to overcome this challenge is to approximate the full-order model (anchor) with a simplified, reduced-order model (template), while performing control synthesis. Unfortunately it is challenging to make formal guarantees about the safety of an anchor model using a controller designed in an online fashion using a template model. To address this problem, this paper proposes a method to generate safety-preserving controllers for anchor models by performing reachability analysis on template models while bounding the modeling error. This paper describes how this reachable set can be incorporated into a Model Predictive Control framework to select controllers that result in safe walking on the anchor model in an online fashion. The method is illustrated on a 5-link RABBIT model, and is shown to allow the robot to walk safely while utilizing controllers designed in an online fashion.