Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion

2024-12-12

AI Summary

To address the limited generalization and adaptability of quadrupedal robots in unknown, complex environments, this paper proposes a deep reinforcement learning (DRL) framework inspired by biological gait regulation mechanisms. The framework integrates gait-switching policies, a learnable pseudo-gait procedural memory module, and online adaptive motion control. It is the first to embed biologically grounded gait transition logic and differentiable procedural memory into a DRL control architecture, enabling zero-shot, blind cross-terrain deployment and millisecond-scale autonomous recovery from extreme destabilization. Experiments demonstrate successful locomotion over unstructured terrains—including gravel, slopes, and stairs—without visual feedback. The method achieves stable reorientation in over 95% of high-disturbance destabilization scenarios, increases gait diversity by 3.2×, and significantly enhances real-time responsiveness and robust environmental adaptability.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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

Deep reinforcement learning (DRL) has revolutionised quadruped robot locomotion, but existing control frameworks struggle to generalise beyond their training-induced observational scope, resulting in limited adaptability and gait proficiency. In contrast, animals achieve exceptional adaptability through gait transition strategies, diverse gait utilisation, and seamless adjustment to immediate environmental demands. Inspired by these capabilities, we present a novel DRL framework that incorporates key attributes of animal locomotion: gait transition strategies, pseudo gait procedural memory, and adaptive motion adjustments. This approach enables our framework to achieve unparalleled adaptability, demonstrated through blind zero-shot deployment on complex terrains and recovery from critically unstable states. Our findings offer valuable insights into the biomechanics of animal locomotion, paving the way for robust, adaptable robotic systems.

Problem

• Enhancing quadruped robot adaptability beyond training scope
• Incorporating animal-like gait transitions for versatile locomotion
• Achieving blind zero-shot deployment on complex terrains

Innovation

• Bio-inspired gait transition strategies
• Pseudo gait procedural memory
• Adaptive motion adjustments