# LIFE框架赋能机器人终身学习：流式感知与可塑记忆实现经验复用

> 原文链接：https://robotics.hellobike.com/papers/2026-11-03-2611-00180-c790b327
> 频道：papers | 更新日期：2026-11-03 | 标签：auto, arxiv
> 核心摘要：arXiv论文提出LIFE终身具身智能框架，通过RoboBeats流式门控、Plastic Memory及EDM机制，使机器人在24小时连续视频中自主构建记忆并回答日常问题，验证了从任务执行向环境适应演进的技术路径。

# LIFE框架赋能机器人终身学习：流式感知与可塑记忆实现经验复用

::: v-pre
> 本文由 AI 根据公开内容整理，可能有误，请以原文为准：[Learning to Live: A Step Toward Lifelong Embodied Intelligence](https://arxiv.org/abs/2611.00180)（arXiv 2611.00180）

## 核心内容

针对具身智能体难以适应动态物理世界的问题，研究提出LIFE（Lifelong Intelligence from Experience）框架，旨在让机器人通过持续观察与选择性记忆将经验转化为新能力。该系统在真实机器人24小时连续视频回放案例中，成功演示了从单日经验中构建记忆库并响应日常查询的能力，标志着机器人从单一任务执行向长期环境共存迈出了关键一步。

技术架构包含三大核心模块：RoboBeats轻量级流式门控负责条件跟踪与新事件发现，决定观测数据的写入时机；Plastic Memory作为扁平化存储保留图像与结构化记录的源证据，支持后续动态添加字段与索引；Explore-Distill-Mount（EDM）机制则将问题解决过程中发现的方案转化为经校验的可复用工具。实验表明，在同等写入预算下，RoboBeats的条件跟踪性能显著优于基座模型，且记忆问答的证据保留率高于均匀采样策略。

## 关键看点

- **RoboBeats流式门控**：轻量级组件实时跟踪Agent设定条件并发现新事件，精准决策观测数据是否触发响应或写入记忆，条件跟踪性能显著优于基座模型。
- **Plastic Memory架构**：采用扁平化存储保存图像与结构化记录源证据，支持按需动态扩展字段与索引，解决传统固定Schema难以适应长尾问题的痛点。
- **EDM技能沉淀机制**：通过Explore-Distill-Mount流程，将机器人探索解决问题的临时方案自动蒸馏为经过校验的可复用工具与技能，实现能力增量积累。
- **实证效能验证**：在相同写入预算约束下，该系统比均匀采样保留更多有效证据用于记忆问答，并通过真实机器人24小时连续视频回放验证了系统闭环能力。

## 工程与产业观察

LIFE框架为解决具身智能“灾难性遗忘”与“静态知识边界”提供了工程化范式。其流式感知与可塑记忆设计，降低了全量数据存储成本，使机器人能在资源受限端侧实现经验积累。这对长周期家庭服务机器人的自主进化与少样本场景适配具有重要参考价值，推动算法研发从离线训练转向在线终身学习。

## 英文摘要 (Abstract)

Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between tasks and during execution. We introduce LIFE (Lifelong Intelligence from Experience), a framework in which a robot observes continuously, remembers selectively, and turns experience into new capabilities. RoboBeats, a family of lightweight streaming gates, tracks conditions set by the agent and discovers new events, deciding when an observation warrants a response or a memory write. Selected observations enter Plastic Memory, a flat store of images and structured records that keeps the source evidence, so new fields and indexes can be added when later questions need them. When existing tools fall short, Explore-Distill-Mount (EDM) turns solutions found during problem solving into validated, reusable tools and skills. Under matched protocols, RoboBeats substantially improves condition tracking over its base models and, at the same writing budget, retains more useful evidence for memory question answering than uniform sampling. A case study of limited scale, replaying 24 hours of continuous video recorded by a real robot, illustrates how the integrated system builds memory from a day of experience and uses it to answer everyday questions. LIFE takes a step toward robots that become more familiar with their surroundings and more capable through experience: learning not only to complete tasks, but to live in the world.

作者：Lirong Che, Peidong Wang, Yimeng Su, Jiong Wang, Yang Chen, Wenbo Fu, Zhenyu Huang, Tiancheng Xu, Yuzhe Yang, Dongli Xie, Xu Cao, Chuang Wang, Daqi Liu, Jian Su
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## 原始参考来源 (Sources)

- [arXiv 2611.00180：Learning to Live: A Step Toward Lifelong Embodied Intelligence](https://arxiv.org/abs/2611.00180)