Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914259217154048 |
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| author | Cheng, Shuo Ma, Liqian Chen, Zhenyang Mandlekar, Ajay Garrett, Caelan Xu, Danfei |
| author_facet | Cheng, Shuo Ma, Liqian Chen, Zhenyang Mandlekar, Ajay Garrett, Caelan Xu, Danfei |
| contents | Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose a unified sim-and-real co-training framework for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real-world demonstrations. Central to our approach is learning a domain-invariant, task-relevant feature space. Our key insight is that aligning the joint distributions of observations and their corresponding actions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT)-inspired loss within the co-training framework, and extend this to an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate our method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to a 30% improvement in the real-world success rate and even generalize to scenarios seen only in simulation. Project webpage: https://ot-sim2real.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training Cheng, Shuo Ma, Liqian Chen, Zhenyang Mandlekar, Ajay Garrett, Caelan Xu, Danfei Robotics Artificial Intelligence Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose a unified sim-and-real co-training framework for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real-world demonstrations. Central to our approach is learning a domain-invariant, task-relevant feature space. Our key insight is that aligning the joint distributions of observations and their corresponding actions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT)-inspired loss within the co-training framework, and extend this to an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate our method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to a 30% improvement in the real-world success rate and even generalize to scenarios seen only in simulation. Project webpage: https://ot-sim2real.github.io/. |
| title | Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18631 |