Article

LEXP: Learning Robotic Manipulation from Physical Experience via Context-Space Adaptation

The authors propose LEXP, a framework for learning from physical experience through context-space adaptation that combines task-level planning with an experience module, reusing execution experience to generate contextual feedback while model weights and the action interface remain fixed. Across 12 real-world manipulation tasks, LEXP achieved 23/36 successes (63.9%), compared with 19/36 (52.8%) using memory alone and 2/36 (5.6%) for vanilla ChatGPT-6-Astra.

Part of the event: LEXP: Improving Robot Manipulation with Experiential Context Without Updating Weights

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