Visual and tactile pretraining have been extensively studied in dexterous robot manipulation tasks. However, existing methods typically require the simultaneous acquisition of visual and tactile data, making it difficult to utilize low-cost, unpaired visual-tactile datasets. Moreover, these methods often rely on tactile sensors to provide input data for reinforcement learning (RL) during the physical deployment of robotic dexterous hands, which highly increases deployment costs. To address these challenges, we propose UpViTaL, an unpaired visualtactile self-supervised representation learning method for RLbased robot dexterous manipulation. Specifically, we collect low-cost unpaired visual and tactile datasets for manipulation skill learning using a camera and tactile gloves on three robot manipulation tasks. The temporal tactile self-supervised representation learning module of UpViTaL is used to explore efficient tactile representations from time-series tactile data. In parallel, the visual pretraining module of UpViTaL helps to extract efficient visual representations from visual data. In addition, we fuse unpaired visual-tactile representations through an RL reward mechanism, which does not require robotic dexterous hands tactile sensors for practical deployment. We validate our approach on three dexterous robot manipulation tasks. Experimental results demonstrate that UpViTaL can efficiently learn robot manipulation skills. Compared to existing approaches for visual pretraining, our method significantly improves the success rate by more than 30%.

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.

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.