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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.14759 |
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| _version_ | 1866909913303744512 |
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| author | Intelligence, Physical Amin, Ali Aniceto, Raichelle Balakrishna, Ashwin Black, Kevin Conley, Ken Connors, Grace Darpinian, James Dhabalia, Karan DiCarlo, Jared Driess, Danny Equi, Michael Esmail, Adnan Fang, Yunhao Finn, Chelsea Glossop, Catherine Godden, Thomas Goryachev, Ivan Groom, Lachy Hancock, Hunter Hausman, Karol Hussein, Gashon Ichter, Brian Jakubczak, Szymon Jen, Rowan Jones, Tim Katz, Ben Ke, Liyiming Kuchi, Chandra Lamb, Marinda LeBlanc, Devin Levine, Sergey Li-Bell, Adrian Lu, Yao Mano, Vishnu Mothukuri, Mohith Nair, Suraj Pertsch, Karl Ren, Allen Z. Sharma, Charvi Shi, Lucy Xiaoyang Smith, Laura Springenberg, Jost Tobias Stachowicz, Kyle Stoeckle, Will Swerdlow, Alex Tanner, James Torne, Marcel Vuong, Quan Walling, Anna Wang, Haohuan Williams, Blake Yoo, Sukwon Yu, Lili Zhilinsky, Ury Zhou, Zhiyuan |
| author_facet | Intelligence, Physical Amin, Ali Aniceto, Raichelle Balakrishna, Ashwin Black, Kevin Conley, Ken Connors, Grace Darpinian, James Dhabalia, Karan DiCarlo, Jared Driess, Danny Equi, Michael Esmail, Adnan Fang, Yunhao Finn, Chelsea Glossop, Catherine Godden, Thomas Goryachev, Ivan Groom, Lachy Hancock, Hunter Hausman, Karol Hussein, Gashon Ichter, Brian Jakubczak, Szymon Jen, Rowan Jones, Tim Katz, Ben Ke, Liyiming Kuchi, Chandra Lamb, Marinda LeBlanc, Devin Levine, Sergey Li-Bell, Adrian Lu, Yao Mano, Vishnu Mothukuri, Mohith Nair, Suraj Pertsch, Karl Ren, Allen Z. Sharma, Charvi Shi, Lucy Xiaoyang Smith, Laura Springenberg, Jost Tobias Stachowicz, Kyle Stoeckle, Will Swerdlow, Alex Tanner, James Torne, Marcel Vuong, Quan Walling, Anna Wang, Haohuan Williams, Blake Yoo, Sukwon Yu, Lili Zhilinsky, Ury Zhou, Zhiyuan |
| contents | We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP), that provides for RL training of VLAs via advantage conditioning. Our method incorporates heterogeneous data into the self-improvement process, including demonstrations, data from on-policy collection, and expert teleoperated interventions provided during autonomous execution. RECAP starts by pre-training a generalist VLA with offline RL, which we call $π^{*}_{0.6}$, that can then be specialized to attain high performance on downstream tasks through on-robot data collection. We show that the $π^{*}_{0.6}$ model trained with the full RECAP method can fold laundry in real homes, reliably assemble boxes, and make espresso drinks using a professional espresso machine. On some of the hardest tasks, RECAP more than doubles task throughput and roughly halves the task failure rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $π^{*}_{0.6}$: a VLA That Learns From Experience Intelligence, Physical Amin, Ali Aniceto, Raichelle Balakrishna, Ashwin Black, Kevin Conley, Ken Connors, Grace Darpinian, James Dhabalia, Karan DiCarlo, Jared Driess, Danny Equi, Michael Esmail, Adnan Fang, Yunhao Finn, Chelsea Glossop, Catherine Godden, Thomas Goryachev, Ivan Groom, Lachy Hancock, Hunter Hausman, Karol Hussein, Gashon Ichter, Brian Jakubczak, Szymon Jen, Rowan Jones, Tim Katz, Ben Ke, Liyiming Kuchi, Chandra Lamb, Marinda LeBlanc, Devin Levine, Sergey Li-Bell, Adrian Lu, Yao Mano, Vishnu Mothukuri, Mohith Nair, Suraj Pertsch, Karl Ren, Allen Z. Sharma, Charvi Shi, Lucy Xiaoyang Smith, Laura Springenberg, Jost Tobias Stachowicz, Kyle Stoeckle, Will Swerdlow, Alex Tanner, James Torne, Marcel Vuong, Quan Walling, Anna Wang, Haohuan Williams, Blake Yoo, Sukwon Yu, Lili Zhilinsky, Ury Zhou, Zhiyuan Machine Learning Robotics We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP), that provides for RL training of VLAs via advantage conditioning. Our method incorporates heterogeneous data into the self-improvement process, including demonstrations, data from on-policy collection, and expert teleoperated interventions provided during autonomous execution. RECAP starts by pre-training a generalist VLA with offline RL, which we call $π^{*}_{0.6}$, that can then be specialized to attain high performance on downstream tasks through on-robot data collection. We show that the $π^{*}_{0.6}$ model trained with the full RECAP method can fold laundry in real homes, reliably assemble boxes, and make espresso drinks using a professional espresso machine. On some of the hardest tasks, RECAP more than doubles task throughput and roughly halves the task failure rate. |
| title | $π^{*}_{0.6}$: a VLA That Learns From Experience |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2511.14759 |