arXiv cs.AI· Josef Chen·· 14 小时前AI 评分31
更多规划反而更糟：面向世界模型的学习型规划器研究
When More Planning Hurts: Learned Planners for World Models
AI 导读
在 PushT 基准上，一个仅 1,505 参数的动作更新器解决了 16 个预留场景中的 8 个，而已发布的 LePlanner 解决全部 16 个，总规划与编码时间仅 5.652s，该动作更新器为 59.079s。匹配实验中，将后续更新预算从 4 增至 8、12 次，成功数反而从 9 降至 8、7，额外更新未挽救任何失败任务，成败变化集中在一个场景簇内。
正文 · 原文
Abstract:On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection. The comparison planners perform as follows: Selected Adam (7 tasks solved, total planning and encoding time 218.440s), adapted iCEM (2 tasks solved, total planning and encoding time 49.309s), released LePlanner (16 tasks solved, total planning and encoding time 5.652s). The total planning and encoding time for our action updater is 59.079s. Note that our action updater does not satisfy the task-cost criterion we fixed before evaluation. We also conduct another matched experiment where we only replay the first ten commands in each context and then allow the next solve 4, 8 or 12 updates (subsequent solves all get 8 updates). Among all 16 contexts (8 scene clusters), the 3 budgets get 9, 8 and 7 successes, respectively. Interestingly, the extra updates did not rescue any failing task in this experiment. All the changes in success/failure happen in one scene cluster, where we observe that the extra updates decrease the costs of the selected latent world-model trajectories for our updater, while the tasks that used to be successes become failures. The code, weights, evaluators, execution traces and cost accounting accompany the paper.
| Comments: | 23 pages, 9 figures, 16 tables, 28 references |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.37891 [cs.RO] |
| (or arXiv:2610.37891v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37891 arXiv-issued DOI via DataCite |
Submission history
From: Josef Liyanjun Chen [view email]
[v1]
Tue, 27 Oct 2026 19:32:05 UTC (177 KB)
来源：arXiv cs.AI · arxiv.org