Agile asymmetric multi-legged locomotion: contact planning via geometric mechanics and spin model duality

2026-02-09

AI Summary

This work addresses the challenge of high-dimensional contact coordination in multi-legged robots, which hinders the exploitation of leg redundancy due to the lack of a systematic control framework. By integrating geometric mechanics with the spin-model duality from statistical mechanics, the authors formulate complex locomotion planning as a graph optimization problem and leverage symmetry breaking to guide the generation of optimal asymmetric gaits. The approach reveals that symmetry reconstruction in high-dimensional embodied systems can yield highly efficient locomotion strategies, enabling hardware simplification—specifically, reducing actuation requirements on individual legs. Experimentally validated on a hexapod robot, the method achieves a forward speed of 0.61 body lengths per cycle, representing a 50% improvement over conventional gaits, and demonstrates that high-performance locomotion can be maintained with actuation on only two legs on one side of the body.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• 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
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Legged robot research is presently focused on bipedal or quadrupedal robots, despite capabilities to build robots with many more legs to potentially improve locomotion performance. This imbalance is not necessarily due to hardware limitations, but rather to the absence of principled control frameworks that explain when and how additional legs improve locomotion performance. In multi-legged systems, coordinating many simultaneous contacts introduces a severe curse of dimensionality that challenges existing modeling and control approaches. As an alternative, multi-legged robots are typically controlled using low-dimensional gaits originally developed for bipeds or quadrupeds. These strategies fail to exploit the new symmetries and control opportunities that emerge in higher-dimensional systems. In this work, we develop a principled framework for discovering new control structures in multi-legged locomotion. We use geometric mechanics to reduce contact-rich locomotion planning to a graph optimization problem, and propose a spin model duality framework from statistical mechanics to exploit symmetry breaking and guide optimal gait reorganization. Using this approach, we identify an asymmetric locomotion strategy for a hexapod robot that achieves a forward speed of 0.61 body lengths per cycle (a 50% improvement over conventional gaits). The resulting asymmetry appears at both the control and hardware levels. At the control level, the body orientation oscillates asymmetrically between fast clockwise and slow counterclockwise turning phases for forward locomotion. At the hardware level, two legs on the same side remain unactuated and can be replaced with rigid parts without degrading performance. Numerical simulations and robophysical experiments validate the framework and reveal novel locomotion behaviors that emerge from symmetry reforming in high-dimensional embodied systems.

Problem

• multi-legged locomotion
• curse of dimensionality
• symmetry breaking
• contact coordination
• gait optimization

Innovation

• geometric mechanics
• spin model duality
• asymmetric locomotion
• multi-legged robots
• symmetry breaking