AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation

• URL: http://arxiv.org/abs/2506.19269v2
• Date: Wed, 25 Jun 2025 05:10:04 GMT
• Title: AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation
• Authors: Ziyan Zhao, Ke Fan, He-Yang Xu, Ning Qiao, Bo Peng, Wenlong Gao, Dongjiang Li, Hui Shen,
• Abstract summary: AnchorDP3 is a diffusion policy framework for dual-arm robotic manipulation.<n>It is trained on large-scale, procedurally generated simulation data.<n>It achieves a 98.7% average success rate in the RoboTwin benchmark.
• Score: 8.603450327406879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present AnchorDP3, a diffusion policy framework for dual-arm robotic manipulation that achieves state-of-the-art performance in highly randomized environments. AnchorDP3 integrates three key innovations: (1) Simulator-Supervised Semantic Segmentation, using rendered ground truth to explicitly segment task-critical objects within the point cloud, which provides strong affordance priors; (2) Task-Conditioned Feature Encoders, lightweight modules processing augmented point clouds per task, enabling efficient multi-task learning through a shared diffusion-based action expert; (3) Affordance-Anchored Keypose Diffusion with Full State Supervision, replacing dense trajectory prediction with sparse, geometrically meaningful action anchors, i.e., keyposes such as pre-grasp pose, grasp pose directly anchored to affordances, drastically simplifying the prediction space; the action expert is forced to predict both robot joint angles and end-effector poses simultaneously, which exploits geometric consistency to accelerate convergence and boost accuracy. Trained on large-scale, procedurally generated simulation data, AnchorDP3 achieves a 98.7% average success rate in the RoboTwin benchmark across diverse tasks under extreme randomization of objects, clutter, table height, lighting, and backgrounds. This framework, when integrated with the RoboTwin real-to-sim pipeline, has the potential to enable fully autonomous generation of deployable visuomotor policies from only scene and instruction, totally eliminating human demonstrations from learning manipulation skills.

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