Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery

TL;DR

This paper introduces a hierarchical framework combining reactive decision-making and motion planning to enable bipedal robots to recover from external disturbances in real-time, ensuring stable and collision-free locomotion.

Contribution

It presents a novel integration of linear-temporal-logic-based reactive synthesis with trajectory optimization for robust, real-time perturbation recovery in bipedal robots.

Findings

Successfully recovers from diverse perturbations

Generates stable and collision-free motions

Demonstrates effectiveness in real-time scenarios

Abstract

In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot's full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes.