Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations

TL;DR

This paper introduces a novel STL-guided model predictive control framework for bipedal robots, enhancing robustness and task satisfaction during external disturbances and complex maneuvers, outperforming existing controllers in simulations.

Contribution

It is the first to apply STL-guided trajectory optimization for bipedal push recovery, integrating task guarantees with robustness quantification.

Findings

Outperforms state-of-the-art controllers in dynamic simulations.

Successfully handles complex maneuvers like crossed-leg recovery.

Demonstrates versatility in tasks like stepping on disjointed footholds.

Abstract

This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion push recovery, where the robot experiences unexpected disturbances. Existing recovery strategies often struggle with complex task logic reasoning and locomotion robustness evaluation, making them susceptible to failures caused by inappropriate recovery strategies or insufficient robustness. To address this issue, the STL-guided framework generates optimal and safe recovery trajectories that simultaneously satisfy the task specification and maximize the locomotion robustness. Our framework outperforms a state-of-the-art locomotion controller in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Furthermore, it demonstrates versatility in tasks such as locomotion on stepping stones, where the robot must select from a set of disjointed footholds to maneuver successfully.