PocketDP3: Efficient Pocket-Scale 3D Visuomotor Policy
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912862556913664 |
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| author | Zhang, Jinhao Zhou, Zhexuan Li, Huizhe Lai, Yichen Xia, Wenlong Song, Haoming Gong, Youmin Mei, Jie |
| author_facet | Zhang, Jinhao Zhou, Zhexuan Li, Huizhe Lai, Yichen Xia, Wenlong Song, Haoming Gong, Youmin Mei, Jie |
| contents | Recently, 3D vision-based diffusion policies have shown strong capability in learning complex robotic manipulation skills. However, a common architectural mismatch exists in these models: a tiny yet efficient point-cloud encoder is often paired with a massive decoder. Given a compact scene representation, we argue that this may lead to substantial parameter waste in the decoder. Motivated by this observation, we propose PocketDP3, a pocket-scale 3D diffusion policy that replaces the heavy conditional U-Net decoder used in prior methods with a lightweight Diffusion Mixer (DiM) built on MLP-Mixer blocks. This architecture enables efficient fusion across temporal and channel dimensions, significantly reducing model size. Notably, without any additional consistency distillation techniques, our method supports two-step inference without sacrificing performance, improving practicality for real-time deployment. Across three simulation benchmarks--RoboTwin2.0, Adroit, and MetaWorld--PocketDP3 achieves state-of-the-art performance with fewer than 1% of the parameters of prior methods, while also accelerating inference. Real-world experiments further demonstrate the practicality and transferability of our method in real-world settings. Code will be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22018 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PocketDP3: Efficient Pocket-Scale 3D Visuomotor Policy Zhang, Jinhao Zhou, Zhexuan Li, Huizhe Lai, Yichen Xia, Wenlong Song, Haoming Gong, Youmin Mei, Jie Robotics Recently, 3D vision-based diffusion policies have shown strong capability in learning complex robotic manipulation skills. However, a common architectural mismatch exists in these models: a tiny yet efficient point-cloud encoder is often paired with a massive decoder. Given a compact scene representation, we argue that this may lead to substantial parameter waste in the decoder. Motivated by this observation, we propose PocketDP3, a pocket-scale 3D diffusion policy that replaces the heavy conditional U-Net decoder used in prior methods with a lightweight Diffusion Mixer (DiM) built on MLP-Mixer blocks. This architecture enables efficient fusion across temporal and channel dimensions, significantly reducing model size. Notably, without any additional consistency distillation techniques, our method supports two-step inference without sacrificing performance, improving practicality for real-time deployment. Across three simulation benchmarks--RoboTwin2.0, Adroit, and MetaWorld--PocketDP3 achieves state-of-the-art performance with fewer than 1% of the parameters of prior methods, while also accelerating inference. Real-world experiments further demonstrate the practicality and transferability of our method in real-world settings. Code will be released. |
| title | PocketDP3: Efficient Pocket-Scale 3D Visuomotor Policy |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.22018 |