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Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation

Authors
Teng Xue, Alberto Rigo, Bingjian Huang, Jiayi Shen, Zhengtong Xu, Nick Colonnese, et al.
Source
arXiv
Published
Collection
Embodied AI
Contact Rich ManipulationVisual Tactile LearningDiffusion PolicyReactive ControlDexterous Manipulation
Open arXivMarkdown source

Summary

Tube Diffusion Policy (TDP) learns a reactive visual-tactile manipulation policy for contact-rich tasks. It combines diffusion for chunk-start action generation with a learned streaming feedback flow so the robot can adjust actions at each step instead of running an open-loop action chunk.

Problem

  • Existing imitation learning methods for manipulation often generate multi-step action chunks and execute them with limited feedback inside the chunk.
  • This hurts contact-rich manipulation because object geometry, friction, and contact events are uncertain, while tactile sensing arrives at high frequency and needs fast action updates.
  • Slow diffusion inference also makes per-timestep replanning hard, which reduces robustness to disturbances and model mismatch.

Approach

  • TDP replaces a fixed action chunk with an action tube: a nominal action initialization plus local feedback corrections during execution.
  • At the start of each chunk horizon, a diffusion model denoises to produce an initial action that accounts for nonlinear contact dynamics.
  • During execution, a learned observation-conditioned velocity field streams later actions from fresh visual and tactile observations, giving step-wise corrections inside the chunk.
  • The method uses a dual-time design: diffusion time for denoising and trajectory time for real-time action evolution.
  • The control view is inspired by tube MPC, but TDP learns the feedback flow directly from demonstrations and does not require an explicit dynamics model.

Results

  • The paper claims TDP consistently outperforms state-of-the-art imitation learning baselines on the Push-T benchmark and three additional visual-tactile dexterous manipulation tasks.
  • It reports two real-world experiments showing stronger reactivity under contact uncertainty and external disturbances.
  • The paper states that the action-tube step-wise correction reduces the required denoising steps, which improves suitability for real-time, high-frequency feedback control.
  • The provided excerpt does not include quantitative numbers such as success rates, error metrics, denoising-step counts, or exact baseline margins.

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