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MemBodied adds fixed-size episodic memory to vision-language-action policies

huggingface.co by Tej Deep Pala et al. Editor score 8/10

MemBodied combines an interaction state with an episode anchor and reports 7.81 times the mean success rate of a stateless policy on five memory-dependent tasks, using 10 times fewer added parameters than the strongest memory-augmented baseline. The results make its compact alternative to growing context worth examining for robot policies.

What

MemBodied adds fixed-size episodic memory to vision-language-action policies using an associative state and an episode anchor.

Method

At each policy call, action generation conditions on the current input and both memory components instead of directly using past observations.

Why it matters

Compact memory offers a way to retain task history without growing context and inference latency as observations accumulate.

Read on huggingface.co → Code · 9 stars

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