HapticWorld：具有实时扭矩反馈的交互式世界模拟器
HapticWorld: an Interactive World Simulator with Real-time Torque Feedback
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中文总结 AI 辅助
HapticWorld是一个交互式世界模拟器，通过实时预测并反馈关节扭矩，在接触丰富任务中使数据收集吞吐量提升1.6倍，训练的策略在真实世界中达到54/60成功率，接近真实数据水平，并可作为F/T条件策略评估平台。
中文摘要 AI 辅助
接触丰富的操作依赖于力感知，而力感知很难仅从视觉信号中推断出来，无论是在收集演示数据还是在训练策略时都是如此。然而，带有力标注的数据仍然难以大规模获取：真实机器人收集将每个演示都绑定到物理硬件上，物理模拟器报告的接触力与真实测量值存在系统性偏差，而学习型世界模拟器虽然可扩展且逼真，但仅基于视觉，因此操作员在数据收集过程中感觉不到任何力，数据也不携带力/扭矩（F/T）标签。我们提出了HapticWorld，一个交互式世界模拟器，它能够预测关节扭矩以及观测结果，并实时将其反馈给操作员，从而在人类和学习到的世界模型之间建立触觉闭环。在三个接触丰富的任务中，扭矩反馈使数据收集吞吐量平均提高了1.6倍。基于HapticWorld生成的演示训练的策略在60次真实世界试验中成功了54次，接近真实世界数据的56/60上限，远远超过了仅视觉基线的19/60成功率。此外，在HapticWorld内部测量的成功率与真实世界评估结果高度吻合，这表明HapticWorld可以作为一个独立的、基于F/T条件的策略评估平台。
英文摘要
Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training policies. Force-annotated data, however, remains hard to obtain at scale: real-robot collection ties every demonstration to physical hardware, physics simulators report contact forces that deviate systematically from real measurements, and learned world simulators, though scalable and realistic, are vision-only, so operators feel nothing during data collection and the data carries no force/torque (F/T) labels. We present HapticWorld, an interactive world simulator that predicts joint torque together with observations and renders it back to the operator in real time, closing the haptic loop between a human and a learned world model. Across three contact-rich tasks, torque feedback raises data collection throughput by 1.6 times on average. Policies trained on HapticWorld-generated demonstrations succeed in 54/60 real-world trials, approaching the 56/60 upper bound of real-world data, and far exceeding the 19/60 success rate of the vision-only baseline. Moreover, the success rates measured inside HapticWorld closely match real-world evaluation, demonstrating that HapticWorld can serve as a stand-alone F/T-conditioned policy evaluation platform.
