VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation

• URL: http://arxiv.org/abs/2501.03606v1
• Date: Tue, 07 Jan 2025 08:14:53 GMT
• Title: VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation
• Authors: Zhengnan Sun, Zhaotai Shi, Jiayin Chen, Qingtao Liu, Yu Cui, Qi Ye, Jiming Chen,
• Abstract summary: Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination.<n>Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills.<n>We introduce VTAO-BiManip, a novel framework that combines visual-tactile-action pretraining with object understanding to facilitate curriculum RL to enable human-like bimanual manipulation.
• Score: 8.882764358932276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual dexterous manipulation remains significant challenges in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In this paper, we introduce VTAO-BiManip, a novel framework that combines visual-tactile-action pretraining with object understanding to facilitate curriculum RL to enable human-like bimanual manipulation. We improve prior learning by incorporating hand motion data, providing more effective guidance for dual-hand coordination than binary tactile feedback. Our pretraining model predicts future actions as well as object pose and size using masked multimodal inputs, facilitating cross-modal regularization. To address the multi-skill learning challenge, we introduce a two-stage curriculum RL approach to stabilize training. We evaluate our method on a bottle-cap unscrewing task, demonstrating its effectiveness in both simulated and real-world environments. Our approach achieves a success rate that surpasses existing visual-tactile pretraining methods by over 20%.

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