World In-Context Learning for Agentic Robot Manipulation
Overview
This paper introduces World In-Context Learning (WICL), a framework where a frozen model learns manipulation tasks through simulation rehearsal and context updates instead of modifying network weights. The authors present RoboScript, a modular infrastructure that implements WICL to transfer skills across visual distributions and embodiments. Evaluated on 50 tasks from the RoboTwin 2.0 benchmark, RoboScript achieves an 83.4% mean success rate using only one demonstration per task without gradient updates, outperforming strong trained action policies by over 20 percentage points. The method generalizes across visual domains, improves via reflection, scales with foundation models, and transfers to a physical dual-arm robot through cross-embodiment memory adaptation. All code is available at https://github.com/ArcheOne-AI/RoboScript.
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Reported quantity · improvement over baseline: 20 other · Basis not reported
“n and randomized settings with one demonstration per task and no gradient updates, exceeding the strongest trained action policy by over 20 points. The method generalizes across visual distributions, improves through reflection, scales with the foundation model, and transfers to a physical dual-arm robot through cross-embodiment memory adaptation. With a world in the loop, a robot can learn a manipulation task in its”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks: 50 other · Basis not reported
“modular infrastructure that implements WICL for agentic manipulation. From a single demonstration, RoboScript learns each task through rehearsal in simulation, freezes the learned knowledge as a Task Memory, and deploys the frozen memory on new scenes with data isolation that ensures every deployment input is available on a real robot. On all 50 tasks of RoboTwin 2.0, RoboScript reaches 83.4% mean success across clea”
Exact source · revision 1Source owner not reported
Reported quantity · Demonstrations / episodes: 1 other · Basis not reported
“n and randomized settings with one demonstration per task and no gradient updates, exceeding the strongest trained action policy by over 20 points. The method generalizes across visual distributions, improves through reflection, scales with the foundation model, and transfers to a physical dual-arm robot through cross-embodiment memory adaptation. With a world in the loop, a robot can learn a manipulation task in its”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“context as well as it learns one in its weights. All code is available at https://github.com/ArcheOne-AI/RoboScript”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWorld In-Context Learning for Agentic Robot Manipulation
This paper introduces World In-Context Learning (WICL), a framework where a frozen model learns manipulation tasks through simulation rehearsal and context updates instead of modifying network weights. The authors present RoboScript, a modular infrastructure that implements WICL to transfer skills across visual distributions and embodiments. Evaluated on 50 tasks from the RoboTwin 2.0 benchmark, RoboScript achieves an 83.4% mean success rate using only one demonstration per task without gradient updates, outperforming strong trained action policies by over 20 percentage points. The method generalizes across visual domains, improves via reflection, scales with foundation models, and transfers to a physical dual-arm robot through cross-embodiment memory adaptation. All code is available at https://github.com/ArcheOne-AI/RoboScript.
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