中文标题#
基于硬件的零先验知识方法在肌腱驱动四足机器人运动控制中的终身学习实现
英文标题#
Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
中文摘要#
像哺乳动物一样，机器人必须在对自身身体结构和周围环境知识不完整的情况下，迅速学会控制自己的身体并与环境互动。 它们还必须适应两者持续的变化。 这项工作提出了一种受生物启发的学习算法，通用到特定（G2P），应用于自行开发和制造的肌腱驱动四足机器人系统。 我们的四足机器人经历了一个初始五分钟的泛化运动 babbling 阶段，随后进行 15 次优化试验（每次持续 20 秒），以实现特定的周期性运动。 这一过程模仿了哺乳动物中观察到的探索 - 利用范式。 每次优化，机器人都会逐步改进其初始的 “足够好” 的解决方案。 我们的结果作为一个概念验证，展示了硬件在环系统能够在几分钟内学习控制具有冗余性的肌腱驱动四足机器人，以实现功能性和适应性的非凸周期性运动。 通过推进机器人运动的自主控制，我们的方法为能够动态适应新环境的机器人铺平了道路，确保持续的适应性和性能。
英文摘要#
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.
文章页面#
基于硬件的零先验知识方法在肌腱驱动四足机器人运动控制中的终身学习实现
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