arXiv cs.AI· Yuhai Wang, Jiawei Xia, Bowen Sun, Rongxuan Zhou, Xiao Hu, Yongliang Shi, Jing Du, Yang Ye·· 14 小时前AI 评分31
PRISM：在世界模型中用先验引导想象采样
PRISM: PRior-guided Imagination Sampling in world Models
AI 导读
PRISM 提出从冻结的世界模型编码器特征中提取目标条件动作先验，用先验的均值和预测不确定性引导动作搜索，再由世界模型评估动作后果是否契合目标。在四个模拟控制任务和两个真实机器人平台上，轻量动作头提升了规划性能，同一融合接口无需修改即可接入基于相同冻结特征的扩散先验。
正文 · 原文
Abstract:Latent world models learn representations that enable them to predict how actions change the environment - a capability we call physical intuition. We find that these same representations also support action intuition: a lightweight head can learn a goal-conditioned action prior directly from the frozen encoder's features. We introduce PRISM to investigate how action intuition can be extracted from frozen world-model representations and used to guide goal-conditioned planning. The prior's mean and predicted uncertainty guide the search for useful actions, while the world model evaluates their consequences against the goal. Experiments on four simulated control tasks and two real-robot platforms show that a lightweight action head improves planning performance, and that the same fusion interface accepts a diffusion prior over the same frozen features without modification. These results demonstrate the potential of reusing world-model representations to both predict action consequences and guide action selection.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.07974 [cs.RO] |
| (or arXiv:2606.07974v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07974 arXiv-issued DOI via DataCite |
Submission history
From: Yang Ye [view email]
[v1]
Sat, 6 Jun 2026 04:18:10 UTC (11,128 KB)
[v2]
Sat, 24 Oct 2026 23:45:30 UTC (4,118 KB)
来源：arXiv cs.AI · arxiv.org