Learning from Physical Experience: Context-Space Adaptation for Robotic Manipulation
Overview
This research introduces LEXP, a framework that enables general-purpose reasoners like ChatGPT-6-Astra to perform physical manipulation tasks by adapting to context through experience. Across 12 real-world manipulation tasks, LEXP achieved 23 out of 36 successful trials (63.9%), significantly outperforming memory-only approaches (52.8%) and vanilla Astra (5.6%). The system combines task-level planning with an experience module that extracts lessons from execution evidence and provides contextual feedback for subsequent actions while keeping model weights and action interfaces fixed. Results show that both task-level planning and retained experience are critical components, as removing either reduces success rates substantially.
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Event evidence and corrections
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Reported quantity · total trials: 36 other · reported trials
“erate contextual feedback for subsequent actions. Model weights and the action interface remain fixed throughout. Across 12 real-world manipulation tasks, LEXP achieves $23/36$ successful trials (63.9\%), compared with $19/36$ (52.8\%) with memory alone and $2/36$ (5.6\%) for vanilla Astra. On three tasks, bread transfer, sort trash and toiletries, and retrieve drawer, removing planning reduces success from $7/9$ to”
Exact source · revision 1Source owner not reported
Reported quantity · total trials (memory alone): 36 other · reported trials
“erate contextual feedback for subsequent actions. Model weights and the action interface remain fixed throughout. Across 12 real-world manipulation tasks, LEXP achieves $23/36$ successful trials (63.9\%), compared with $19/36$ (52.8\%) with memory alone and $2/36$ (5.6\%) for vanilla Astra. On three tasks, bread transfer, sort trash and toiletries, and retrieve drawer, removing planning reduces success from $7/9$ to”
Exact source · revision 1Source owner not reported
Reported quantity · total trials (vanilla Astra): 36 other · reported trials
“erate contextual feedback for subsequent actions. Model weights and the action interface remain fixed throughout. Across 12 real-world manipulation tasks, LEXP achieves $23/36$ successful trials (63.9\%), compared with $19/36$ (52.8\%) with memory alone and $2/36$ (5.6\%) for vanilla Astra. On three tasks, bread transfer, sort trash and toiletries, and retrieve drawer, removing planning reduces success from $7/9$ to”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLearning from Physical Experience: Context-Space Adaptation for Robotic Manipulation
This research introduces LEXP, a framework that enables general-purpose reasoners like ChatGPT-6-Astra to perform physical manipulation tasks by adapting to context through experience. Across 12 real-world manipulation tasks, LEXP achieved 23 out of 36 successful trials (63.9%), significantly outperforming memory-only approaches (52.8%) and vanilla Astra (5.6%). The system combines task-level planning with an experience module that extracts lessons from execution evidence and provides contextual feedback for subsequent actions while keeping model weights and action interfaces fixed. Results show that both task-level planning and retained experience are critical components, as removing either reduces success rates substantially.
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