AMP1 零樣本在真機試驗中完成一百零四次

Computer Science > Robotics arXiv:2611.09892 (cs) [Submitted on 6 Nov 2026] Title:On Agentic Manipulation Policies Authors:Chen Chen, Omid Bagherieh, Naveen Nikamanth Appiah Balaji, Jesse T.Gonzalez, Negin Heravi, Fahad Islam, Xueting Li, Jean-Baptiste Regli, Kejia Ren, Petr Švec, David J.

Yoon, Matthias Müller, Vladlen Koltun View a PDF of the paper titled On Agentic Manipulation Policies, by Chen Chen and 12 other authors View PDF HTML (experimental) Abstract:Recent work on robot manipulation has focused on specialist policies trained on demonstrations.

We show that generalist AI models can control manipulators more reliably than specialist policies such as vision-language-action models (VLAs) and world-action models (WAMs).

We systematically analyze agentic manipulation policies (AMPs), in which a generalist AI model commands the manipulator directly, adapting its actions as the task unfolds.We present AMP1, a reference harness that runs unchanged across single-arm, bimanual, and mobile manipulation.

AMP1 achieves state-of-the-art results on the RoboLab-120 and CaP-Gym benchmarks, with no demonstrations or task-specific training.On RoboLab-120, AMP1 reaches an 85.6\% success rate zero-shot, compared to 49.2\% and 42.9\% for leading VLA and WAM policies.

The agent succeeds zero-shot in 104 of 108 real-world trials spanning twelve tasks and three robot instances, reducing failure rates by 20x and 15x relative to strong VLA and WAM baselines.

The basic harness supplies no grasping, pushing, or placing skills; the agent develops its own strategies, such as searching for hidden objects, using objects as tools, and calibrating its cameras.

With the addition of a persistent memory store, the agent can also learn from experience: an augmented harness with a memory representation that is carried across episodes lowers the failure rate on RoboDojo bimanual manipulation tasks by 4.6x relative to zero-shot deployment.

Our analysis suggests that AMPs are substantially more general and robust than specialist manipulation policies, providing a promising foundation for robot manipulation.Comments: Videos and code available at: this https URL Subjects: Robotics (cs.RO); Artificial Intelligence (cs.

AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2611.09892 [cs.RO] (or arXiv:2611.09892v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2611.

09892 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Matthias Müller [view email] [v1] Fri, 6 Nov 2026 18:59:34 UTC (24,323 KB) Full-text links: Access Paper: View a PDF of the paper titled On Agentic Manipulation Policies, by Chen Chen and 12 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

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