REMM: Revisable Embodied Memory for Manipulation
We introduce REMM, Revisable Embodied Memory for Manipulation, a supervisory memory layer for a frozen G0.5 vision-language-action executor.
ProofPaper ↗
Key points
- Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence.
- REMM separates an append-only evidence history from revisable task state and explicitly links derived relations and completed conditions to their supporting facts.
- Complementary video diagnostics show improved grounding of retained history into the next instruction, including comparisons with $π{0.5}$-compatible high-level planner adaptations.
- These results support revisable memory as a practical interface for continued manipulation without policy retraining.
Sources (1)
- [1]REMM: Revisable Embodied Memory for ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:42 PM
We introduce REMM, Revisable Embodied Memory for Manipulation, a supervisory memory layer for a frozen G0.5 vision-language-action executor.
Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence.
Extractive summary: sentences quoted from the sources.
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