机器人零先验知识学习自适应步态，5分钟内完成训练
Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
- 采用生物启发的通用到特定学习算法，通过探索-利用循环迭代优化
- 5分钟泛化探索+15次20秒精调，实现非凸周期运动控制
- 适合需要快速适应新环境的自主移动机器人研究
与哺乳动物类似，机器人必须在缺乏身体结构和环境完整知识的情况下，快速学会控制自身并与其环境互动，同时适应持续变化。本文提出一种生物启发的学习算法——通用到特定（G2P），应用于自主设计与制造的腱驱动四足机器人系统。该机器人经历初始5分钟的泛化运动探索（电机拨弄），随后进行15次每次20秒的精调试验，以实现特定周期性运动。这一过程模拟了哺乳动物的探索-利用范式。每次精调后，机器人逐步改进初始的“足够好”解。结果证明，该软硬件协同系统仅用数分钟即可学习具有冗余性的腱驱动四足机器人的控制，实现功能性和自适应的非凸周期运动。该方法推动了机器人行走中自主控制的发展，为机器人在动态环境中持续调整、保持适应性与性能提供了可能。
原文摘要 · Abstract (English)
Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 refinement trials (each lasting 20 seconds) to achieve specific cyclical movements. This process mirrors the exploration-exploitation paradigm observed in mammals. With each refinement, the robot progressively improves upon its initial "good enough" solution. Our results serve as a proof-of-concept, demonstrating the hardware-in-the-loop system's ability to learn the control of a tendon-driven quadruped with redundancies in just a few minutes to achieve functional and adaptive cyclical non-convex movements. By advancing autonomous control in robotic locomotion, our approach paves the way for robots capable of dynamically adjusting to new environments, ensuring sustained adaptability and performance.
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