通用操作接口：无需野外机器人的野外机器人教学
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
AI总结：
本研究提出通用操作接口UMI框架，通过手持夹爪收集野外人类演示数据，结合推理延迟匹配、相对轨迹动作表示实现硬件无关的机器人策略学习，可零样本泛化到新场景新物体，已开源软硬件系统。
摘要：
我们提出了通用操作接口（Universal Manipulation Interface，UMI）——这是一个数据收集与策略学习框架，可实现从野外人类演示到可部署机器人策略的直接技能迁移。UMI采用手持夹爪，配合精心设计的接口，能够为具有挑战性的双手动态操作演示提供便携、低成本且信息丰富的数据收集方案。为推动可部署策略的学习，UMI整合了经过精心设计的策略接口，具备推理时延迟匹配特性，还采用了相对轨迹动作表示方法。由此学习得到的策略具备硬件无关性，可部署于多种机器人平台。凭借这些特性，UMI框架解锁了全新的机器人操作能力，仅需为每个任务更换训练数据，就能实现零样本泛化的动态、双手、精准及长时序行为。我们通过全面的真实世界实验验证了UMI的多功能性与有效性：当基于多样化人类演示训练时，通过UMI学习的策略可零样本泛化到新环境与新物体。UMI的软硬件系统已在https://umi-gripper.github.io开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
