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EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning

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arxiv 2407.01479 v2 pith:LBHH4IAT submitted 2024-07-01 cs.RO cs.LG

Jingyun Yang , Zi-ang Cao , Congyue Deng , Rika Antonova , Shuran Song , Jeannette Bohg This is my paper
classification cs.ROcs.LG
keywords learningdataapproachdiffusionenvironmentsequibotequivariantgeneralizable
verification ladder T0 review T1 audit T2 compute T3 formal
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Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning. We propose Equibot, a robust, data-efficient, and generalizable approach for robot manipulation task learning. Our approach combines SIM(3)-equivariant neural network architectures with diffusion models. This ensures that our learned policies are invariant to changes in scale, rotation, and translation, enhancing their applicability to unseen environments while retaining the benefits of diffusion-based policy learning such as multi-modality and robustness. We show on a suite of 6 simulation tasks that our proposed method reduces the data requirements and improves generalization to novel scenarios. In the real world, with 10 variations of 6 mobile manipulation tasks, we show that our method can easily generalize to novel objects and scenes after learning from just 5 minutes of human demonstrations in each task.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A robot policy that propagates a retrieved contact point through predicted object poses, producing a time-varying affordance trajectory, improves manipulation success over static affordance and pose-only baselines.

  2. Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    SIDO morphs static demonstrations into counterfactual future-pose samples, training a goal-conditioned policy that, paired with a pose predictor, grasps objects whose motion was unseen during training.

  3. Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Continuous multi-view image-space keypoint trajectories plus per-camera equivariant augmentation beat strong 3D and image baselines on MimicGen and real UR5 tasks.

  4. UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies

    cs.RO 2025-10 conditional novelty 6.0 of 10

    Embodiment-Aware Diffusion Policy steers a UMI-trained diffusion policy with controller tracking-cost gradients at inference time, improving aerial manipulation success in simulation and real flights.

  5. 4D Visual Pre-training for Robot Learning

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A next-frame point-cloud diffusion pre-training method (FVP) improves DP3 and RDT-1B manipulation success rates on the paper's own tasks.

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