MemTransfer: Benchmarking Memory Beyond Matched Experience in Embodied Decision-Making
Overview
This research introduces MemTransfer, a benchmark assessing six memory representations and a working-memory baseline for embodied agents using a frozen vision-language policy. Evaluated across 100 navigation cases in a simulated warehouse with expert demonstrations, the study compares performance under varying starting poses, route availability, and history relevance. Results indicate that while Full-context and Episodic memory achieve high success at original demonstration starts, they suffer significant drops when conditions change, whereas Summary and Working memory show different robustness profiles depending on the number of demonstrations and task blocking scenarios.
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Environment: simulation
“navigation cases across ten task types in a simulated warehouse, with expert demonstrations supplying the history. Three comparisons vary the starting pose, route availability, and amount and task relevance of history. With one demonstration per task, Full-context and Episodic memory reach 95.3% and 100.0% success at the original demonstration start, but lose 48-49 percentage points at a new test start. Summary chang”
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Reported quantity · loss in success percentage points: 48 other · Basis not reported
“navigation cases across ten task types in a simulated warehouse, with expert demonstrations supplying the history. Three comparisons vary the starting pose, route availability, and amount and task relevance of history. With one demonstration per task, Full-context and Episodic memory reach 95.3% and 100.0% success at the original demonstration start, but lose 48-49 percentage points at a new test start. Summary chang”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMemTransfer: Benchmarking Memory Beyond Matched Experience in Embodied Decision-Making
This research introduces MemTransfer, a benchmark assessing six memory representations and a working-memory baseline for embodied agents using a frozen vision-language policy. Evaluated across 100 navigation cases in a simulated warehouse with expert demonstrations, the study compares performance under varying starting poses, route availability, and history relevance. Results indicate that while Full-context and Episodic memory achieve high success at original demonstration starts, they suffer significant drops when conditions change, whereas Summary and Working memory show different robustness profiles depending on the number of demonstrations and task blocking scenarios.
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