# ProWorld：面向闭环策略评估的带本体感受反馈多视角世界模型

> 原文链接：https://robotics.hellobike.com/papers/2026-10-11-2610-15294
> 频道：papers | 更新日期：2026-10-11 | 标签：arxiv, cs.RO
> 核心摘要：为降低机器人操作策略实机评估成本，ProWorld联合预测多视角视频与机器人状态，并在TriWorldBench和本地任务上验证。

# ProWorld：面向闭环策略评估的带本体感受反馈多视角世界模型

::: v-pre
> 原文链接：[ProWorld: Multi-View World Model with Proprioceptive Feedback for Closed-Loop Policy Evaluation](https://arxiv.org/abs/2610.15294)（arXiv 2610.15294 · 发布日期：2026-10-11）

**ProWorld: Multi-View World Model with Proprioceptive Feedback for Closed-Loop Policy Evaluation**

为降低机器人操作策略实机评估成本，ProWorld联合预测多视角视频与机器人状态，并在TriWorldBench和本地任务上验证。

## 核心要点

- 动机：重复实机试验成本高，闭环模型需反馈视角与本体状态并保留失败结果。
- 方法：联合预测多视角视频和机器人状态，将两者反馈给冻结策略进行重复交互。
- 训练与结构：共享transformer预测视频-状态流，并用失败交互减少成功率高估。
- 结果：ProWorld总体TriWorldBench得分最高，生成与真实成功率相关性达0.95/0.94。

**验证范围与局限**：验证范围包括TriWorldBench，以及三种策略、四个本地操作任务上的生成环境与真实机器人成功率对比。

## 英文摘要 (Abstract)

Evaluating robot manipulation policies through repeated physical trials is costly. We study whether an action-conditioned world model can serve as a closed-loop test environment whose policy success rates track those on real robots. Beyond realistic video, the model must return the camera views and proprioceptive state needed for the policy's next action and preserve failed-action outcomes to avoid overestimating policy capability. We introduce ProWorld, a world model that jointly predicts multi-view video and robot state, then feeds both back to a frozen policy for repeated interaction. ProWorld comprises a shared transformer for joint video-state flow prediction and Shared-Time View Encoding to distinguish cameras while preserving temporal synchronization. To reduce interaction hallucinations that turn missed grasps into successful lifts, we augment training with failed interactions collected in high-fidelity simulation, reducing inflated success rates in the generated environment. ProWorld achieves the highest overall TriWorldBench score among the compared methods. Across three policies and four local manipulation tasks, it attains a Pearson correlation of 0.95 and a Spearman correlation of 0.94 between generated and real success rates. These results support joint visual-proprioceptive feedback and failure-aware training as a basis for world-model-based policy evaluation.

作者：Zijun Wang, Tianyi Yan, Haokun Lin, Panwen Hu, Bohang He, Siyu Ren, Xiaoyu Guo, Weijia Liufu, Shuoshuo Xue, Fengjiao Chen
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## 原始参考来源 (Sources)

- [arXiv 2610.15294：ProWorld: Multi-View World Model with Proprioceptive Feedback for Closed-Loop Policy Evaluation](https://arxiv.org/abs/2610.15294)