Direct Action-Manifold Modeling for Efficient and Robust Bimanual Manipulation
Overview
This paper introduces the Direct Manifold Action Transformer (DMAT), a method that reformulates bimanual action generation as direct modeling of observation-conditioned joint-space action chunks to eliminate iterative inference latency. By anchoring predictions with local 3D geometry and applying State-Consistent Spectral-Time Refinement, DMAT achieves single-step inference while maintaining robustness under physical interventions. Evaluated across 567 standard and out-of-distribution trials, DMAT demonstrates superior aggregate success rates and faster generation speeds compared to Improved 3D Diffusion Policy (IDP3) and DiTFlow3D, establishing a new paradigm for efficient bimanual manipulation.
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Reported quantity · trials: 567 other · reported trials
“ctory-straightening procedures. Within DMAT, local 3D geometry anchors predictions to spatial structure, while State-Consistent Spectral-Time Refinement (STR) reconciles predicted chunks with proprioception and suppresses high-frequency residuals. Across 567 standard and out-of-distribution (OOD) trials per method, DMAT outperformed Improved 3D Diffusion Policy (IDP3) and DiTFlow3D in aggregate success rate, achievin”
Exact source · revision 1Source owner not reported
Reported quantity · speedup factor: 2.06 other · Basis not reported · Differing source assertions
“g relative improvements of 129.1% and 95.7%, respectively, while generating action chunks 2.06x and 1.34x faster and remaining robust under repeated 50 N interventions. These results pave the way for a novel generative paradigm for efficient and robust bimanual manipulation.”
Exact source · revision 1Source owner not reported
Reported quantity · speedup factor: 1.34 other · Basis not reported · Differing source assertions
“g relative improvements of 129.1% and 95.7%, respectively, while generating action chunks 2.06x and 1.34x faster and remaining robust under repeated 50 N interventions. These results pave the way for a novel generative paradigm for efficient and robust bimanual manipulation.”
Exact source · revision 1Source owner not reported
Reported quantity · intervention force: 50 other · Basis not reported
“g relative improvements of 129.1% and 95.7%, respectively, while generating action chunks 2.06x and 1.34x faster and remaining robust under repeated 50 N interventions. These results pave the way for a novel generative paradigm for efficient and robust bimanual manipulation.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDirect Action-Manifold Modeling for Efficient and Robust Bimanual Manipulation
This paper introduces the Direct Manifold Action Transformer (DMAT), a method that reformulates bimanual action generation as direct modeling of observation-conditioned joint-space action chunks to eliminate iterative inference latency. By anchoring predictions with local 3D geometry and applying State-Consistent Spectral-Time Refinement, DMAT achieves single-step inference while maintaining robustness under physical interventions. Evaluated across 567 standard and out-of-distribution trials, DMAT demonstrates superior aggregate success rates and faster generation speeds compared to Improved 3D Diffusion Policy (IDP3) and DiTFlow3D, establishing a new paradigm for efficient bimanual manipulation.
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