将动物步态策略融入强化学习，让四足机器人无感适应复杂地形。
Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion
- 基于生物力学设计步态切换、记忆与实时调整机制
- 无需外部传感器即可在真实多地形零样本部署
- 显著优于基线控制器，兼顾效率与稳定性
腿式机器人需适应未知环境中的步态变化，而动物却能轻松应对。现有深度强化学习方法多依赖固定步态，难以适应地形与动态状态变化。本文提出一种受生物启发的DRL控制框架，融合动物运动的三大核心机制：步态转换策略、步态记忆和实时运动调整，使机器人可流畅切换多种步态并恢复失稳状态，全程无需外部传感。框架以生物力学启发的指标（效率、稳定性、系统极限）统一指导最优步态选择。结果表明，该方法可在多样真实地形上实现盲态零样本部署，性能显著超越基线控制器。本工作通过将动物运动智能嵌入数据驱动控制，推动了鲁棒、高效、通用的机器人行走发展，揭示了生物智慧对下一代自适应机器人的塑造潜力。
原文摘要 · Abstract (English)
Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines.
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