ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915669916778496 |
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| author | Chen, Wendi Xue, Han Wang, Yi Zhou, Fangyuan Lv, Jun Jin, Yang Tang, Shirun Wen, Chuan Lu, Cewu |
| author_facet | Chen, Wendi Xue, Han Wang, Yi Zhou, Fangyuan Lv, Jun Jin, Yang Tang, Shirun Wen, Chuan Lu, Cewu |
| contents | Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slow global context, while force sensing captures rapid, high-frequency local contact dynamics. Integrating these signals is challenging due to their fundamental frequency and informational disparities. In this work, we propose ImplicitRDP, a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network. We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens, allowing the policy to perform closed-loop adjustments at the force frequency while maintaining the temporal coherence of action chunks. Furthermore, to mitigate modality collapse where end-to-end models fail to adjust the weights across different modalities, we propose Virtual-target-based Representation Regularization. This auxiliary objective maps force feedback into the same space as the action, providing a stronger, physics-grounded learning signal than raw force prediction. Extensive experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both vision-only and hierarchical baselines, achieving superior reactivity and success rates with a streamlined training pipeline. Code and videos will be publicly available at https://implicit-rdp.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning Chen, Wendi Xue, Han Wang, Yi Zhou, Fangyuan Lv, Jun Jin, Yang Tang, Shirun Wen, Chuan Lu, Cewu Robotics Artificial Intelligence Machine Learning Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slow global context, while force sensing captures rapid, high-frequency local contact dynamics. Integrating these signals is challenging due to their fundamental frequency and informational disparities. In this work, we propose ImplicitRDP, a unified end-to-end visual-force diffusion policy that integrates visual planning and reactive force control within a single network. We introduce Structural Slow-Fast Learning, a mechanism utilizing causal attention to simultaneously process asynchronous visual and force tokens, allowing the policy to perform closed-loop adjustments at the force frequency while maintaining the temporal coherence of action chunks. Furthermore, to mitigate modality collapse where end-to-end models fail to adjust the weights across different modalities, we propose Virtual-target-based Representation Regularization. This auxiliary objective maps force feedback into the same space as the action, providing a stronger, physics-grounded learning signal than raw force prediction. Extensive experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both vision-only and hierarchical baselines, achieving superior reactivity and success rates with a streamlined training pipeline. Code and videos will be publicly available at https://implicit-rdp.github.io. |
| title | ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.10946 |