利用预训练潜在表示在灵巧机器人手上的少样本模仿学习
Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
AI总结：
提出利用大规模任务无关数据集预训练潜在表示并结合基于transformer的行为克隆方法，解决了灵巧机器人手少样本模仿学习问题，提升了抗噪性并成功部署于真实23Dof系统。
摘要：
在应用于灵巧机器人手的模仿学习背景下，系统的高复杂性使得学习复杂的操作任务具有挑战性。然而，描绘人类手部在各种不同任务中的众多数据集可以为我们提供关于人类手部运动的更好知识。我们提出了一种方法，利用多个大规模任务无关数据集来获取潜在表示，这些表示有效地编码了运动子轨迹，我们将其包含在基于transformer的行为克隆方法中。我们的结果表明，与传统的行为克隆方法相比，采用潜在表示可产生增强的性能，特别是在对感知和本体感觉中的错误和噪声的弹性方面。此外，所提出的方法仅依赖于人类演示，消除了对遥操作的需求，因此加速了数据获取过程。用于指尖重定向的精确逆运动学确保了从人类手部数据到机器人的精确转移，促进了操作策略的有效学习和部署。最后，训练的策略已成功转移到真实世界的23Dof机器人系统。
英文摘要：
In the context of imitation learning applied to dexterous robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transformer-based behavior cloning method. Our results demonstrate that employing latent representations yields enhanced performance compared to conventional behavior cloning methods, particularly regarding resilience to errors and noise in perception and proprioception. Furthermore, the proposed approach solely relies on human demonstrations, eliminating the need for teleoperation and, therefore, accelerating the data acquisition process. Accurate inverse kinematics for fingertip retargeting ensures precise transfer from human hand data to the robot, facilitating effective learning and deployment of manipulation policies. Finally, the trained policies have been successfully transferred to a real-world 23Dof robotic system.
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。
