中文标题#
基於硬體的零先驗知識方法在肌腱驅動四足機器人運動控制中的終身學習實現
英文標題#
Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
中文摘要#
像哺乳動物一樣，機器人必須在對自身身體結構和周圍環境知識不完整的情況下，迅速學會控制自己的身體並與環境互動。 它們還必須適應兩者持續的變化。 這項工作提出了一種受生物啟發的學習算法，通用到特定（G2P），應用於自行開發和製造的肌腱驅動四足機器人系統。 我們的四足機器人經歷了一個初始五分鐘的泛化運動 babbling 階段，隨後進行 15 次優化試驗（每次持續 20 秒），以實現特定的周期性運動。 這一過程模仿了哺乳動物中觀察到的探索 - 利用範式。 每次優化，機器人都會逐步改進其初始的 “足夠好” 的解決方案。 我們的結果作為一個概念驗證，展示了硬體在環系統能夠在幾分鐘內學習控制具有冗餘性的肌腱驅動四足機器人，以實現功能性和適應性的非凸周期性運動。 通過推進機器運動的自主控制，我們的方法為能夠動態適應新環境的機器人鋪平了道路，確保持續的適應性和性能。
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.
文章頁面#
基於硬體的零先驗知識方法在肌腱驅動四足機器人運動控制中的終身學習實現
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