Temporal-Spatial Representation Fusion for Dexterous Manipulation Learning with Unpaired Visual-Action Data

Abstract

Supervised behavioral cloning using robot visual-action data has been widely investigated in robot manipulation. However, these methods typically require simultaneous acquisition of visual and action data, which makes them difficult to utilize unpaired visual-action datasets: e.g. videos on Internet or action only data which has less privacy and security concerns. To take advantage of the action data without synchronized visual observation, we propose UnVALe, a novel dexterous robotic manipulation RL framework that utilizes action data without paired images to learn priors of human dexterous manipulation skills. Specifically, an LSTM-based network is designed to learn the temporal action prior by reconstructing the input trajectories, and a VAE network is designed to learn the spatial action prior by reconstructing the input action. Novel rewards are proposed to incorporate the priors into reinforcement learning, which encourages action output from RL polices to maintain low reconstruction errors in the LSTM and VAE networks. We perform extensive validation on three dexterous robot manipulation tasks. The experimental results show that UnVALe can effectively improve robot manipulation performance. Compared with existing visual pretraining methods, our method achieves a more than 30% increase in success rates.