Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion

2025-09-27

AI Summary

Quadrupedal robots exhibit limited locomotion adaptability on dynamic, unknown terrains and often rely on heuristic footstep selection or computationally intensive optimization. Method: This paper proposes an integrated planning framework that unifies symbolic-level reactive synthesis with mixed-integer convex programming (MICP) to generate physically feasible, safe, and robust gaits with real-time adjustability. Contribution/Results: We introduce a novel symbolic repair mechanism and a latency-aware coordination strategy, significantly reducing online optimization overhead and enabling seamless integration of offline synthesis and online execution. Extensive simulation and hardware experiments demonstrate that the system autonomously detects locomotion skill gaps—e.g., on scattered stepping stones or exposed rebar—and achieves safe, dynamic adaptive walking. The approach markedly improves robustness and real-time performance in complex, unstructured environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing methods often depend on heuristics for real-time foothold selection-limiting robustness and adaptability-or rely on computationally intensive trajectory optimization across complex terrains and long horizons. In contrast, our approach combines reactive synthesis for generating correct-by-construction symbolic-level controllers with mixed-integer convex programming (MICP) for dynamic and physically feasible footstep planning during each symbolic transition. To reduce the reliance on costly MICP solves and accommodate specifications that may be violated due to physical infeasibility, we adopt a symbolic repair mechanism that selectively generates only the required symbolic transitions. During execution, real-time MICP replanning based on actual terrain data, combined with runtime symbolic repair and delay-aware coordination, enables seamless bridging between offline synthesis and online operation. Through extensive simulation and hardware experiments, we validate the framework's ability to identify missing locomotion skills and respond effectively in safety-critical environments, including scattered stepping stones and rebar scenarios.

Problem

• Generating physically feasible controllers for terrain-adaptive locomotion
• Reducing computational cost of foothold planning on complex terrains
• Bridging offline synthesis with online replanning for dynamic environments

Innovation

• Reactive synthesis for symbolic-level controllers
• Mixed-integer convex programming for footstep planning
• Symbolic repair mechanism for transition optimization