论文/arXiv 2610.18699·15 天前

RoboFQE：从离线轨迹无 Rollout 评估视觉机器人策略

RoboFQE用离线机器人轨迹进行无Rollout离策略评估，在仿真与真实操纵任务中验证策略回报估计。

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原文链接：RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories（arXiv 2610.18699 · 发布日期：2026-10-13）

RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories

RoboFQE用离线机器人轨迹进行无Rollout离策略评估，在仿真与真实操纵任务中验证策略回报估计。

核心要点

• 部署前评估机器人策略通常依赖真实执行、仿真器或世界模型产生新轨迹。
• RoboFQE结合拟合Q评估、预训练视觉基础模型和学习式终端成功标注估计折扣期望回报。
• 在仿真和真实操纵任务中，RoboFQE能准确估计折扣回报并保持策略排序。
• 消融显示预训练视觉编码器优于从头训练，学习式终端标注仅轻微降低准确性。

验证范围与局限：验证范围为仿真与真实世界操纵任务，包括长时程操纵任务。

英文摘要 (Abstract)

Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based approaches impose infrastructure requirements; simulator and world-model evaluators additionally face prediction errors that can compound over the evaluation horizon. In this paper, we study off-policy evaluation as an alternative to rollout-based robot-policy evaluation methods. Specifically, we introduce RoboFQE, an evaluator that estimates the discounted expected return of a target policy from offline robot trajectories without estimating a behavior-policy density, combining fitted Q-evaluation with a pretrained vision foundation model and learned terminal success annotation to bridge the gap between standard OPE benchmarks and continuous-control visual manipulation. Controlled experiments across simulated and real-world manipulation tasks show that RoboFQE produces accurate discounted-return estimates and preserves policy rankings even on long-horizon manipulation tasks where video world-model evaluators are degraded by autoregressive prediction errors. Ablation experiments show that pretrained vision encoders substantially outperform from-scratch alternatives, particularly on long-horizon tasks, and that learned terminal success annotation enables evaluation without ground-truth reward labels with only slight reduction of accuracy.

作者：Yunfu Deng, Sangwoo Shin, Brahma S. Pavse, Josiah P. Hanna

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.18699：RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories访问 2026-10-21