On Agentic Manipulation Policies
Overview
This paper presents AMP1, a generalist AI-based manipulation policy that outperforms specialist vision-language-action models (VLAs) and world-action models (WAMs) on RoboLab-120 and CaP-Gym benchmarks. AMP1 achieves an 85.6% zero-shot success rate on RoboLab-120 compared to 49.2% and 42.9% for leading baselines, succeeding in 104 of 108 real-world trials across twelve tasks and three robot instances without demonstrations or task-specific training. The system adapts its actions autonomously, developing strategies such as object search and tool use, and can further improve with a persistent memory store that reduces failure rates on RoboDojo bimanual tasks by 4.6x relative to zero-shot deployment.
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Event evidence and corrections
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Reported quantity · failure rate reduction factor: 20 other · Basis not reported · Differing source assertions
“LA 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 ad”
Exact source · revision 1Source owner not reported
Reported quantity · failure rate reduction factor: 15 other · Basis not reported · Differing source assertions
“LA 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 ad”
Exact source · revision 1Source owner not reported
Reported quantity · failure rate reduction factor: 4.6 other · Basis not reported · Differing source assertions
“dition 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 rob”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalOn Agentic Manipulation Policies
This paper presents AMP1, a generalist AI-based manipulation policy that outperforms specialist vision-language-action models (VLAs) and world-action models (WAMs) on RoboLab-120 and CaP-Gym benchmarks. AMP1 achieves an 85.6% zero-shot success rate on RoboLab-120 compared to 49.2% and 42.9% for leading baselines, succeeding in 104 of 108 real-world trials across twelve tasks and three robot instances without demonstrations or task-specific training. The system adapts its actions autonomously, developing strategies such as object search and tool use, and can further improve with a persistent memory store that reduces failure rates on RoboDojo bimanual tasks by 4.6x relative to zero-shot deployment.
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