Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models

2025-05-16

AI Summary

This work addresses dynamic gait instability in humanoid robots subjected to external disturbances in human-robot coexistence environments. We propose an environment-aware active recovery control framework that leverages environmental structures—such as walls—as active support media: the robot’s manipulator applies controlled contact forces against the wall to augment stability. The framework integrates single-rigid-body model predictive control (SRB-MPC) with hybrid linear inverted pendulum (HLIP) dynamics to jointly enable real-time gait re-planning and disturbance rejection. A novel online contact force optimization scheme and adaptive gait adjustment mechanism further enhance robustness against perturbations. Simulation results demonstrate that the system maintains stable bipedal locomotion at 0.5 m/s while rejecting multi-directional impulsive pushes of up to 100 N lasting 0.2 s. Moreover, it achieves high-fidelity trajectory tracking and rapid post-disturbance recovery even under challenging conditions—including non-orthogonal (inclined) wall contacts and complex disturbance profiles.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control and push recovery for humanoid robots, leveraging the arms for push recovery while dynamically walking. The key innovation is to use the environment, such as walls, to facilitate push recovery by combining Single Rigid Body model predictive control (SRB-MPC) with Hybrid Linear Inverted Pendulum (HLIP) dynamics to enable robust locomotion, push detection, and recovery by utilizing the robot's arms to brace against such walls and dynamically adjusting the desired contact forces and stepping patterns. Extensive simulation results on a humanoid robot demonstrate improved perturbation rejection and tracking performance compared to HLIP alone, with the robot able to recover from pushes up to 100N for 0.2s while walking at commanded speeds up to 0.5m/s. Robustness is further validated in scenarios with angled walls and multi-directional pushes.

Problem

• Developing push recovery for humanoid robots during locomotion
• Combining SRB-MPC and HLIP for robust walking and push recovery
• Utilizing robot arms to brace against walls for stability

Innovation

• Combines SRB-MPC with HLIP dynamics
• Uses arms to brace against walls
• Dynamically adjusts contact forces and stepping