啟動機制：學習具滾動接觸關節的仿生腱驅動手部靈巧策略

摘要

仿生、靈巧的機器人手有潛力複製人類能夠執行的許多任務，並成為一個通用操作平台。最近在強化學習（RL）框架方面的進展在四足動作和靈巧操作任務中取得了顯著的表現。結合基於GPU的高度並行化模擬技術，能夠同時模擬成千上萬個機器人，基於RL的控制器變得更具可擴展性和可操作性。然而，為了將經過RL訓練的策略應用於現實世界，我們需要訓練框架輸出能夠與物理致動器和感測器配合工作的策略，以及一種可以使用易得材料製造但足夠堅固以運行互動策略的硬體平台。本研究介紹了仿生腱驅動的Faive手和其系統架構，該系統利用腱驅動的滾動接觸關節實現了一種可三維打印、堅固的高自由度手部設計。我們對手部的每個元素進行建模，並將其整合到GPU模擬環境中，通過RL訓練一個策略，實現了對一種靈巧的手部內球體旋轉技能的零樣本轉移至實際機器人手。

English

Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand.

啟動機制：學習具滾動接觸關節的仿生腱驅動手部靈巧策略

Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints

摘要

Support