arXiv cs.AI· Erik Bauer, Jakob Thumm, Christopher Agia, Rohan Sinha, Chen Xu, Haruki Nishimura, Jeannette Bohg, Marco Pavone, Masha Itkina·· 12 小时前AI 评分31
Behavior Graphs：用无监督图结构解读机器人部署数据
Behavior Graphs: Unsupervised Graph Structures for Interpreting Robot Deployment Data
AI 导读
研究者提出 behavior graphs，将策略 rollout 压缩为一张可解释的有向图，通过识别相似行为模式、按时间连接并关联结果来构建。在三个真实任务的用户研究中，18 名参与者有 16 人更偏好该图而非 rollout 视频，结论相当但感知时间需求低 40.3%、投入低 37.6%。在 43 个数据集上，其预测进度与真实时间顺序的秩相关提升 22.9%，平均绝对误差降低 53.5%。
正文 · 原文
Abstract:Data flywheels promise continuous improvement via insights from deployment data. Understanding how the deployed model succeeds and fails can steer subsequent data collection towards patching failures and reinforcing successes. However, robot deployments create large volumes of high-dimensional rollout data, which is challenging to understand and confidently ground data collection decisions in. To make deployment data easier to analyze and act on, we propose behavior graphs, which compress policy rollouts into a single interpretable, directed graph. We construct the graphs by learning to identify similar behavior modes across rollouts, connecting the behaviors temporally, and finally linking them to outcomes. In a user study on three real-world tasks, where participants analyzed rollouts to choose a data collection strategy, 16 of 18 preferred behavior graphs to rollout videos, reaching comparable conclusions with 40.3% lower perceived temporal demand and 37.6% lower effort. Beyond interpretability for deployment data, behavior graphs are capable progress predictors: across 43 datasets, they improve the rank correlation between predicted progress and true temporal order by 22.9% and reduce mean absolute error by 53.5% relative to the strongest baseline. As the number of deployed robots grows, behavior graphs lend structure to high-dimensional policy rollouts and make deployment data easier to interpret and reason about.
| Comments: | 8 pages, 6 figures, 3 tables. Project website: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.00405 [cs.RO] |
| (or arXiv:2611.00405v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00405 arXiv-issued DOI via DataCite |
Submission history
From: Erik Bauer [view email]
[v1]
Fri, 30 Oct 2026 21:51:01 UTC (2,030 KB)
来源：arXiv cs.AI · arxiv.org