通用操控界面：野外机器人教学无需野外机器人

Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

February 15, 2024

作者: Cheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, Shuran Song

cs.AI

摘要

我们提出了通用操纵界面（UMI）——一个数据收集和策略学习框架，允许直接从野外人类示范转移到可部署的机器人策略。UMI采用手持夹具结合精心设计的界面，实现了便携、低成本和信息丰富的数据收集，适用于具有挑战性的双手和动态操纵示范。为促进可部署的策略学习，UMI结合了精心设计的策略界面，具有推断时间匹配的延迟和相对轨迹动作表示。由此产生的学习策略与硬件无关，并可在多个机器人平台上部署。UMI框架具备这些功能，解锁了新的机器人操纵能力，实现了零-shot通用的动态、双手、精确和长视程行为，只需为每个任务更改训练数据。我们通过全面的真实世界实验展示了UMI的多功能性和有效性，通过UMI学习的策略在多样的人类示范训练后，零-shot通用于新环境和物体。UMI的硬件和软件系统在https://umi-gripper.github.io上开源。

English

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.