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JAMB: Joint Action-Motion Diffusion for Bimanual Manipulation

Summary

Researchers propose JAMB, a diffusion policy that jointly denoises bimanual actions and future 3D point tracks within a shared Transformer. By grounding multimodal representations in a common spatiotemporal coordinate system, the model lets action and motion hypotheses refine each other during denoising. In RoboTwin 2.0, JAMB reaches 83.4% average success across 16 tasks, beating the strongest baseline by 23.9 points; on three real-world tasks it beats action-only and auxiliary geometry prediction methods by 50.0 and 21.2 points, with better generalization to clutter and out-of-distribution backgrounds.

SourcearXiv RoboticsAuthor: Chuyang Xiao, Peilin Meng, David Held
JAMB: Joint Action-Motion Diffusion for Bimanual Manipulation
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[Submitted on 21 Sep 2026]

Title:JAMB: Joint Action-Motion Diffusion for Bimanual Manipulation

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Abstract:Coordinated bimanual manipulation is challenging because the motion of either arm can alter the shared 3D scene and thereby affect the other arm. Yet most diffusion policies generate actions without explicitly modeling these future geometric consequences, while predictive variants typically use future state only as auxiliary supervision or fixed conditioning. We address this limitation by proposing JAMB, a diffusion policy that jointly denoises bimanual actions and future 3D point tracks. By allowing action and track hypotheses to evolve together within a shared Transformer, each can inform and refine the other throughout denoising. We further ground multimodal representations in a shared spatiotemporal coordinate system to facilitate geometry-aware interaction during joint denoising. We evaluate JAMB on diverse bimanual manipulation tasks in RoboTwin 2.0 and on a real-world robot, comparing it with action-only policies and alternative future-prediction approaches spanning different state representations and learning objectives. Across 16 simulation tasks, JAMB achieves an average success rate of 83.4%, outperforming the strongest baseline by 23.9 percentage points. On three real-world tasks, it outperforms the action-only and auxiliary geometry prediction methods by 50.0 and 21.2 percentage points, respectively. Beyond these performance gains, JAMB shows stronger generalization to cluttered scenes and out-of-distribution backgrounds than the evaluated baselines. Together, these results demonstrate the effectiveness of our joint action-motion modeling framework for coordinated bimanual manipulation. Our project website is available at this https URL

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2609.25322 [cs.RO]

(or arXiv:2609.25322v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.25322

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chuyang Xiao [view email] [v1] Mon, 21 Sep 2026 19:11:38 UTC (11,840 KB)

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Key points and analysis

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Key points

  • Jointly denoises bimanual actions and future 3D point tracks in one shared Transformer, enabling mutual refinement.
  • Grounds multimodal representations in a shared spatiotemporal coordinate system for geometry-aware interaction.
  • Achieves 83.4% average success on 16 RoboTwin 2.0 simulation tasks, +23.9 points over strongest baseline.
  • Real-world gains of +50.0 and +21.2 points over action-only and auxiliary geometry prediction baselines, with improved generalization.

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