Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
<|reference_start|>Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots: We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies. UMI employs hand-held grippers coupled with careful interface design to enable portable, low-cost, and information-rich data collection for challenging bimanual and dynamic manipulation demonstrations. To facilitate deployable policy learning, UMI incorporates a carefully designed policy interface with inference-time latency matching and a relative-trajectory action representation. The resulting learned policies are hardware-agnostic and deployable across multiple robot platforms. Equipped with these features, UMI framework unlocks new robot manipulation capabilities, allowing zero-shot generalizable dynamic, bimanual, precise, and long-horizon behaviors, by only changing the training data for each task. We demonstrate UMI's versatility and efficacy with comprehensive real-world experiments, where policies learned via UMI zero-shot generalize to novel environments and objects when trained on diverse human demonstrations. UMI's hardware and software system is open-sourced at https://umi-gripper.github.io.<|reference_end|>
@article{chi2024universal,
    title={Universal Manipulation Interface: In-The-Wild Robot Teaching Without
  In-The-Wild Robots},
    author={Cheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel,
  Siyuan Feng, Russ Tedrake, Shuran Song},
    journal={arXiv preprint arXiv:2402.10329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10329},
    primaryClass={cs.RO}
}