Universal Manipulation Interface: In-The-Wild Robot Teaching Without
  In-The-Wild Robots
Authors: name: Cheng Chi, name: Zhenjia Xu, name: Chuer Pan, name: Eric Cousineau, name: Benjamin Burchfiel, name: Siyuan Feng, name: Russ Tedrake, name: Shuran Song

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.