Dynamic Bipedal MPC with Foot-level Obstacle Avoidance and Adjustable Step Timing

2025-05-19

AI Summary

This work addresses the challenge of dynamic obstacle avoidance for bipedal robots in unstructured environments. We propose a real-time Model Predictive Control (MPC) framework that, for the first time, enables coordinated 3D obstacle avoidance for both the torso and foot placements while dynamically adjusting gait timing. Methodologically: (1) a tunable step-duration mechanism is introduced to significantly improve torso-level obstacle response speed; (2) a 3D foot-placement obstacle avoidance model is formulated using polygonal convex decomposition and Mixed-Integer Quadratic Programming (MIQP), augmented with soft minimum-travel constraints to mitigate local minima; (3) half-space relaxation combined with center-of-mass (COM) dynamics modeling enables online decision-making for stepping-over and lateral circumvention. Evaluated on Cassie/Digit simulations and the Digit physical platform, the approach achieves >92% success rates for dynamic walking, stepping-over, and lateral obstacle circumvention under complex foot-level obstacles, operating at 100 Hz.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Collision-free planning is essential for bipedal robots operating within unstructured environments. This paper presents a real-time Model Predictive Control (MPC) framework that addresses both body and foot avoidance for dynamic bipedal robots. Our contribution is two-fold: we introduce (1) a novel formulation for adjusting step timing to facilitate faster body avoidance and (2) a novel 3D foot-avoidance formulation that implicitly selects swing trajectories and footholds that either steps over or navigate around obstacles with awareness of Center of Mass (COM) dynamics. We achieve body avoidance by applying a half-space relaxation of the safe region but introduce a switching heuristic based on tracking error to detect a need to change foot-timing schedules. To enable foot avoidance and viable landing footholds on all sides of foot-level obstacles, we decompose the non-convex safe region on the ground into several convex polygons and use Mixed-Integer Quadratic Programming to determine the optimal candidate. We found that introducing a soft minimum-travel-distance constraint is effective in preventing the MPC from being trapped in local minima that can stall half-space relaxation methods behind obstacles. We demonstrated the proposed algorithms on multibody simulations on the bipedal robot platforms, Cassie and Digit, as well as hardware experiments on Digit.

Problem

• Real-time MPC for bipedal robots avoiding body and foot obstacles
• Adjustable step timing for faster body obstacle avoidance
• 3D foot-avoidance with implicit swing trajectory selection

Innovation

• Real-time MPC framework for bipedal robots
• Adjustable step timing for body avoidance
• 3D foot-avoidance with convex decomposition