外观
论文arXiv 2610.0945421 天前
RobotAPO：用于机器人操作视频生成的对抗物理偏好优化
原文链接：RobotAPO: Adversarial Physics Preference Optimization for Robotic Manipulation Video Generation（arXiv 2610.09454 · 发布日期：2026-10-07）
RobotAPO: Adversarial Physics Preference Optimization for Robotic Manipulation Video Generation
针对操作视频物理一致性不足，RobotAPO用对抗物理偏好优化生成视频，并在AgiBot条件和真机回放验证。
核心要点
- 动机是纯视觉真实感优化难以捕捉真实交互中脆弱的物理流形。
- 构建AgiBot-PhysPref偏好数据集，隔离条件匹配的物理违规样本。
- RobotAPO在连续flow-matching去噪空间中进行对抗物理偏好优化。
- 评估显示其提升物理一致性，并在真机回放中提高任务成功率。
验证范围与局限：验证范围为保留的AgiBot条件和真机回放，摘要未说明具体任务和机器人数量。
英文摘要 (Abstract)
Robotic manipulation videos are increasingly used as visual plans for embodied agents, but optimizing purely for visual plausibility often fails to capture the fragile physical manifold of real-world interactions. Even minor physics-violating errors at the interaction boundary, such as interpenetration or premature object motion, can completely invalidate the inferred timing and pose needed for downstream execution. Because standard supervised fine-tuning lacks the direct pressure to penalize these localized failures, we introduce AgiBot-PhysPref. This rigorously curated 10,000-sample preference dataset isolates condition-matched physics violations, turning the generator's own failure distribution into a foundational signal for physical consistency. Building upon this, we propose RobotAPO, an adversarial physics preference optimization framework operating in the continuous flow-matching denoising space. To prevent the policy from merely memorizing static curated failures, RobotAPO employs a lightweight adversarial counterfactual proposer that learns a condition-dependent, physical-failure-biased direction in denoising space. This encourages the model to explore and better respect the physical interaction boundary, all while maintaining a pure prompt-and-reference inference interface without requiring external structural conditioning. Comprehensive evaluations demonstrate that explicitly correcting these localized physics violations improves downstream robot execution from generated videos. On held-out AgiBot conditions, RobotAPO outperforms the strongest controlled internal baseline in physical consistency by 6.8% hard score and 10.0% soft score. Crucially, in real-robot replay, it translates these physical-consistency gains into a 37.4% relative improvement in task success over the strongest controlled internal baseline.
作者：Kerui Li, Zhe Jing, Chenyi Huang, Xiaofeng Wang, Zheng Zhu, Haoming Cui, Huaibo Huang
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