arXiv cs.AI· Zhifeng Gu, Ying Chen, Rui Jiang, Lei Yi, Bing Wang·· 14 小时前AI 评分31
EmbodiExp：面向具身智能体的可组合经验迁移框架
EmbodiExp: Compositional Experience Transfer for Embodied Agents
AI 导读
EmbodiExp 提出一种面向具身智能体的可组合经验迁移框架，通过构建以结果为锚定的子目标经验（含子目标、适用条件、可适配策略与可验证结果），在执行中在线组合经验指导动作生成。该框架利用子目标级结果归因，使失败轨迹也能贡献已验证的局部成功与需规避动作的条件性指导。在多个具身基准上采用基于折叠的协议与冻结经验库的实验验证了其有效性。
正文 · 原文
Abstract:Experience can help embodied agents turn high-level plans into grounded actions, but transferring it across tasks remains challenging: previously effective strategies may be inapplicable to the current state or fail to produce their intended outcomes. We present EmbodiExp, a framework for constructing transferable experiences and composing them online during embodied task execution. Our key observation is that tasks often share subgoals whose completion can be assessed independently of overall task success. We therefore introduce an outcome-grounded subgoal experience that specifies a subgoal, applicability conditions, an adaptable policy, and a verifiable outcome. During execution, applicable experiences guide action generation, while verified outcomes can establish the conditions required by subsequent experiences, enabling online composition. Subgoal-level outcome attribution further enables failure utilization: failed trajectories can still contribute verified local successes and condition-specific guidance on actions to avoid. Experiments on multiple embodied benchmarks under a fold-based protocol with frozen experience banks demonstrate the effectiveness of EmbodiExp.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.39180 [cs.RO] |
| (or arXiv:2610.39180v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39180 arXiv-issued DOI via DataCite |
Submission history
From: Zhifeng Gu [view email]
[v1]
Thu, 29 Oct 2026 02:08:42 UTC (5,752 KB)
来源：arXiv cs.AI · arxiv.org