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arXiv · 2509.20579

Large Pre-Trained Models for Bimanual Manipulation in 3D

Hanna Yurchyk·Wei-Di Chang·Gregory Dudek·David Meger
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Abstract

We investigate the integration of attention maps from a pre-trained Vision Transformer into voxel representations to enhance bimanual robotic manipulation. Specifically, we extract attention maps from DINOv2, a self-supervised ViT model, and interpret them as pixel-level saliency scores over RGB images. These maps are lifted into a 3D voxel grid, resulting in voxel-level semantic cues that are incorporated into a behavior cloning policy. When integrated into a state-of-the-art voxel-based policy, our attention-guided featurization yields an average absolute improvement of 8.2% and a relative gain of 21.9% across all tasks in the RLBench bimanual benchmark.

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Hanna Yurchyk, Wei-Di Chang, Gregory Dudek, David Meger. 2025-09-24. Large Pre-Trained Models for Bimanual Manipulation in 3D. https://doi.org/10.1109/humanoids65713.2025.11203079

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