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Equivariant Diffusion Policy
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abstract
Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this work, we propose Equivariant Diffusion Policy, a novel diffusion policy learning method that leverages domain symmetries to obtain better sample efficiency and generalization in the denoising function. We theoretically analyze the $\mathrm{SO}(2)$ symmetry of full 6-DoF control and characterize when a diffusion model is $\mathrm{SO}(2)$-equivariant. We furthermore evaluate the method empirically on a set of 12 simulation tasks in MimicGen, and show that it obtains a success rate that is, on average, 21.9% higher than the baseline Diffusion Policy. We also evaluate the method on a real-world system to show that effective policies can be learned with relatively few training samples, whereas the baseline Diffusion Policy cannot.
Forward citations
Cited by 8 Pith papers
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Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills
A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.
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Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
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.
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Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Continuous multi-view image-space keypoint trajectories plus per-camera equivariant augmentation beat strong 3D and image baselines on MimicGen and real UR5 tasks.
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Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification
Projecting 3D gripper keypoints onto camera pixels and classifying those pixels yields millimeter-precise, multi-modal closed-loop manipulation faster than diffusion policies.
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SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
SeedPolicy introduces self-evolving gated attention to extend the temporal horizon of diffusion policies, yielding 36.8% and 169% relative gains over standard DP on clean and randomized RoboTwin 2.0 tasks.
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CLASS: Contrastive Learning via Action Sequence Supervision for Robot Manipulation
CLASS pre-training with Diffusion Policy reaches 75% average success under visual shifts where baseline behavior cloning methods fail.
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4D Visual Pre-training for Robot Learning
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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AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation
AnchorDP3 combines simulator-supervised semantic segmentation, task-conditioned encoders, and affordance-anchored keypose diffusion, reporting a 98.7% average success rate on RoboTwin bimanual tasks.
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