# REMM：用于操作的可修订具身记忆

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-12710
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：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。

# REMM：用于操作的可修订具身记忆

::: v-pre
> 原文链接：[REMM: Revisable Embodied Memory for Manipulation](https://arxiv.org/abs/2610.12710)（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 Retaining observations alone does not specify which completed conditions should be withdrawn after a disturbance or which obligations require repair.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
:::

## 原始参考来源 (Sources)

- [arXiv 2610.12710：REMM: Revisable Embodied Memory for Manipulation](https://arxiv.org/abs/2610.12710)