Point Bridge: 3D Representations for Cross Domain Policy Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915890449088512 |
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| author | Haldar, Siddhant Johannsmeier, Lars Pinto, Lerrel Gupta, Abhishek Fox, Dieter Narang, Yashraj Mandlekar, Ajay |
| author_facet | Haldar, Siddhant Johannsmeier, Lars Pinto, Lerrel Gupta, Abhishek Fox, Dieter Narang, Yashraj Mandlekar, Ajay |
| contents | Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulation datasets. Simulation and synthetic data generation offer a scalable alternative, but their usefulness is limited by the visual domain gap between simulation and reality. In this work, we present Point Bridge, a framework that leverages unified, domain-agnostic point-based representations to unlock synthetic datasets for zero-shot sim-to-real policy transfer, without explicit visual or object-level alignment. Point Bridge combines automated point-based representation extraction via Vision-Language Models (VLMs), transformer-based policy learning, and efficient inference-time pipelines to train capable real-world manipulation agents using only synthetic data. With additional co-training on small sets of real demonstrations, Point Bridge further improves performance, substantially outperforming prior vision-based sim-and-real co-training methods. It achieves up to 44% gains in zero-shot sim-to-real transfer and up to 66% with limited real data across both single-task and multitask settings. Videos of the robot are best viewed at: https://pointbridge3d.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16212 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Point Bridge: 3D Representations for Cross Domain Policy Learning Haldar, Siddhant Johannsmeier, Lars Pinto, Lerrel Gupta, Abhishek Fox, Dieter Narang, Yashraj Mandlekar, Ajay Robotics Robot foundation models are beginning to deliver on the promise of generalist robotic agents, yet progress remains constrained by the scarcity of large-scale real-world manipulation datasets. Simulation and synthetic data generation offer a scalable alternative, but their usefulness is limited by the visual domain gap between simulation and reality. In this work, we present Point Bridge, a framework that leverages unified, domain-agnostic point-based representations to unlock synthetic datasets for zero-shot sim-to-real policy transfer, without explicit visual or object-level alignment. Point Bridge combines automated point-based representation extraction via Vision-Language Models (VLMs), transformer-based policy learning, and efficient inference-time pipelines to train capable real-world manipulation agents using only synthetic data. With additional co-training on small sets of real demonstrations, Point Bridge further improves performance, substantially outperforming prior vision-based sim-and-real co-training methods. It achieves up to 44% gains in zero-shot sim-to-real transfer and up to 66% with limited real data across both single-task and multitask settings. Videos of the robot are best viewed at: https://pointbridge3d.github.io/ |
| title | Point Bridge: 3D Representations for Cross Domain Policy Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.16212 |