Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
arxiv(2024)
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.
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