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Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
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Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on dexterous hands, poses significant challenges for effective tactile feature learning, especially for 3D tactile data, as there are no large standardized datasets and no strong pretrained backbones. To address these challenges, we propose a novel canonical representation that reduces the difficulty of 3D tactile feature learning and further introduces a force-based self-supervised pretraining task to capture both local and net force features, which are crucial for dexterous manipulation. Our method achieves an average success rate of 78% across four fine-grained, contact-rich dexterous manipulation tasks in real-world experiments, demonstrating effectiveness and robustness compared to other methods. Further analysis shows that our method fully utilizes both spatial and force information from 3D tactile data to accomplish the tasks. The codes and videos can be viewed at https://3dtacdex.github.io.
Forward citations
Cited by 4 Pith papers
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Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
A force-guided attention module and future-force prediction auxiliary task improve visuo-tactile fusion for dexterous manipulation, reaching 93% average success in real robot trials.
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CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
CordViP achieves strong real-world dexterous manipulation by feeding a diffusion policy with pose-tracked 3D object models and hand point clouds, pretrained on contact maps and arm-hand coordination.
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OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints
OmniManip represents manipulation as canonical-space interaction points and directions, lets a VLM select them under closed-loop verification, and tracks object poses during execution to achieve zero-shot open-vocabul...
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AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion
AdaDexGrasp learns to fuse point clouds with finger-level tactile labels to generate, judge, and correct dexterous grasps, reporting 91%/82%/83% success on seen, unseen-object, and unseen-category sets in simulation.
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