GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909797608062976 |
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| author | Qian, Quanhao Zhao, Guoyang Zhang, Gongjie Wang, Jiuniu Xu, Ran Gao, Junlong Zhao, Deli |
| author_facet | Qian, Quanhao Zhao, Guoyang Zhang, Gongjie Wang, Jiuniu Xu, Ran Gao, Junlong Zhao, Deli |
| contents | Effective robotic manipulation relies on a precise understanding of 3D scene geometry, and one of the most straightforward ways to acquire such geometry is through multi-view observations. Motivated by this, we present GP3 -- a 3D geometry-aware robotic manipulation policy that leverages multi-view input. GP3 employs a spatial encoder to infer dense spatial features from RGB observations, which enable the estimation of depth and camera parameters, leading to a compact yet expressive 3D scene representation tailored for manipulation. This representation is fused with language instructions and translated into continuous actions via a lightweight policy head. Comprehensive experiments demonstrate that GP3 consistently outperforms state-of-the-art methods on simulated benchmarks. Furthermore, GP3 transfers effectively to real-world robots without depth sensors or pre-mapped environments, requiring only minimal fine-tuning. These results highlight GP3 as a practical, sensor-agnostic solution for geometry-aware robotic manipulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation Qian, Quanhao Zhao, Guoyang Zhang, Gongjie Wang, Jiuniu Xu, Ran Gao, Junlong Zhao, Deli Robotics Artificial Intelligence Effective robotic manipulation relies on a precise understanding of 3D scene geometry, and one of the most straightforward ways to acquire such geometry is through multi-view observations. Motivated by this, we present GP3 -- a 3D geometry-aware robotic manipulation policy that leverages multi-view input. GP3 employs a spatial encoder to infer dense spatial features from RGB observations, which enable the estimation of depth and camera parameters, leading to a compact yet expressive 3D scene representation tailored for manipulation. This representation is fused with language instructions and translated into continuous actions via a lightweight policy head. Comprehensive experiments demonstrate that GP3 consistently outperforms state-of-the-art methods on simulated benchmarks. Furthermore, GP3 transfers effectively to real-world robots without depth sensors or pre-mapped environments, requiring only minimal fine-tuning. These results highlight GP3 as a practical, sensor-agnostic solution for geometry-aware robotic manipulation. |
| title | GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15733 |