Attention-DP3: Spatially Object-aware 3D Diffusion Policy via Geometry-aligned Attentional Conditioning
Attention-DP3 adds spatially object-aware attentional conditioning to 3D diffusion policies, improving robotic manipulation by up to 31% under heavy clutter.
Attention-DP3 injects object-level geometric cues into the unchanged DP3 diffusion policy via Tri-field Attentional Conditioning, using targetness, intra-target saliency, and backgroundness fields. Open-vocabulary 2D segmentation masks are lifted to 3D with calibrated camera geometry to build object-centric priors. Experiments on Adroit, DexArt, MetaWorld, and a real-world SO101 platform show state-of-the-art results, outperforming DP3 by up to 31% under heavy distractor clutter; the code is publicly available on GitHub.
- Tri-field attention injects object-level geometric cues into DP3 backbone
- State-of-the-art on Adroit, DexArt, MetaWorld, and real-world SO101
- Outperforms DP3 by up to 31% under heavy clutter
From the original article
1 min read at huggingface.co
3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tightly intermingled with visually similar distractors. As a result, standard 3D diffusion policies often struggle to localize and exploit task-relevant geometry as scene complexity grows.
By Changbo Yan, Zhongbo Zhang, Zaibin Zhang · huggingface.co
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