MemBodied: Recurrent Associative Memory for Vision-Language-Action Models
MemBodied gives vision-language-action policies fixed episodic memory without expanding context.
MemBodied adds a fixed-size episodic memory to vision-language-action policies so history-dependent manipulation does not require storing every past observation. It combines an associative state of interactions across policy calls with an episode anchor that keeps a compact view of the initial scene. On five memory-dependent RMBench tasks it achieves 7.81 times the mean success of a stateless policy and 2.98 times vanilla recurrent memory, beating the strongest memory baseline by 1.3 times with 10 times fewer added parameters. On fully observable LIBERO-Long it scores 90.6%, a 5.4-point gain over the stateless pi0 policy.
- Fixed memory avoids ever-growing context and inference latency
- 7.81 times stateless success on five RMBench memory tasks
- 1.3 times the strongest baseline with 10 times fewer parameters
- LIBERO-Long reaches 90.6%, 5.4 points above stateless pi0
From the original article
1 min read at huggingface.co
Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations.
By Tej Deep Pala, Navonil Majumder, Bryce Goh · huggingface.co
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