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.

本研究针对强化学习（RL）算法在真实环境中训练的挑战，探索了在可控且现实的设置中进行灵巧操作的直接训练。通过基准测试三种RL算法在复杂手持操作任务中的效果，结果展示了RL训练在实际应用中的实用性及相关挑战，为机器人领域的研究者和从业者提供了宝贵的经验和启示。

强化学习方法在三指机械手灵巧操作中的基准测试