外观
论文arXiv 2610.1271020 天前
REMM：用于操作的可修订具身记忆
原文链接：REMM: Revisable Embodied Memory for Manipulation（arXiv 2610.12710 · 发布日期：2026-10-08）
REMM: Revisable Embodied Memory for Manipulation
REMM为冻结G0.5 VLA执行器加入可修订记忆层，在51次物理试验中提升干预恢复与序列回忆进度。
核心要点
- 长程操作需保留任务相关历史，并在物理证据变化后撤回不再支持的结论。
- REMM分离只追加证据历史与可修订任务状态，显式连接结论与支撑事实。
- 支撑事实变化时撤回依赖结论，并在已验证技能边界安排定向恢复。
- 51次物理试验中，干预恢复57%到91%，序列回忆43%到70%。
验证范围与局限：验证限于51次物理试验，包括操作员辅助运行，涉及干预恢复和序列回忆。
英文摘要 (Abstract)
Long-horizon manipulation requires robots to retain task-relevant history while revising conclusions no longer supported by physical evidence. Retaining observations alone does not specify which completed conditions should be withdrawn after a disturbance or which obligations require repair. We introduce REMM, Revisable Embodied Memory for Manipulation, a supervisory memory layer for a frozen G0.5 vision-language-action executor. REMM separates an append-only evidence history from revisable task state and explicitly links derived relations and completed conditions to their supporting facts. When accepted evidence changes a supporting fact, it withdraws dependent conclusions and schedules targeted recovery at verified skill boundaries without updating the executor. Across 51 physical trials, including operator-assisted runs, REMM improves mean recorded task progress over the strongest baseline from 57% to 91% in intervention recovery and from 43% to 70% in sequence recall. 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. Project: https://revanthsenthil.github.io/remm/.
作者：Revanth Krishna Senthilkumaran, Ruwan Wickramarachchi, Alessandro Oltramari, Jonathan Francis
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