使用三指夹爪进行灵巧机器人操作的强化学习方法基准测试
Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper
AI总结：
本研究探讨了在真实环境中直接训练强化学习算法以执行灵巧操作，对三种RL算法在复杂手内操作任务上进行了基准测试，验证了真实世界训练的实用性并分享了实验经验。
摘要：
强化学习（RL）训练主要在成本效益高且受控的仿真环境中进行。然而，将这些训练好的模型迁移到现实世界任务中通常会带来不可避免的挑战。本研究探讨了在受控但真实的现实世界环境中直接训练RL算法，以执行灵巧操作。本文展示了三种RL算法在现实世界实际环境中的复杂手内操作任务上的基准测试结果。我们的研究不仅证明了在真实现实世界场景中进行RL训练的实用性，促进了直接的现实世界应用，还提供了对相关挑战和考量的见解。此外，我们还分享了所采用的实验方法的经验，旨在赋能并吸引该动态机器人领域的其他研究人员和从业者。
英文摘要：
Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores the direct training of RL algorithms in controlled yet realistic real-world settings for the execution of dexterous manipulation. The benchmarking results of three RL algorithms trained on intricate in-hand manipulation tasks within practical real-world contexts are presented. Our study not only demonstrates the practicality of RL training in authentic real-world scenarios, facilitating direct real-world applications, but also provides insights into the associated challenges and considerations. Additionally, our experiences with the employed experimental methods are shared, with the aim of empowering and engaging fellow researchers and practitioners in this dynamic field of robotics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
