[
    {
        "id": "osp-25822",
        "type": "article-journal",
        "title": "HapticWorld: an Interactive World Simulator with Real-time Torque Feedback",
        "author": [
            {
                "family": "Peng",
                "given": "Shaoting"
            },
            {
                "family": "Liang",
                "given": "Litian"
            },
            {
                "family": "Wang",
                "given": "Yixuan"
            },
            {
                "family": "Yang",
                "given": "Ming"
            },
            {
                "family": "Driggs-Campbell",
                "given": "Katherine"
            },
            {
                "family": "Cutkosky",
                "given": "Mark"
            },
            {
                "family": "Xu",
                "given": "James Jingxi"
            }
        ],
        "URL": "https://omanscience.com/en/articles/hapticworld-an-interactive-world-simulator-with-real-time-torque-feedback",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]