mimic-one：面向通用机器人灵巧操作的可扩展模型方案
mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
AI总结：
该研究提出基于扩散模型的mimic-one方案，搭配自研16自由度腱驱动机械手与多接口遥操作数据采集体系，端到端训练的策略在真实操作任务中分布外成功率最高达93.3%，推动灵巧机器人操作技术发展。
摘要：
我们提出了一种基于扩散模型的方案，用于高灵巧人形机器人手的真实世界控制，旨在实现样本高效学习和平滑的精细动作推理。我们的系统配备了一款新设计的16自由度腱驱动机械手，搭载广角腕部相机，安装在Franka Emika Panda机械臂上。我们开发了一套多功能遥操作流程和数据采集协议，同时使用基于数据手套的接口和VR接口，能够在拾取放置、物品分类、装配插入等多样化任务中采集高质量数据。借助高频生成式控制，我们从原始感官输入端到端训练策略，可在复杂操作场景中实现平滑、自校正的运动。真实世界评估显示，其分布外成功率最高达93.3%，由于涌现的自校正行为，性能最高提升了33.3%，同时还揭示了策略性能的缩放趋势。我们的研究通过硬件、学习与真实世界部署的全集成实用方案，推动了灵巧机器人操作领域的前沿发展。
英文摘要：
We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action inference. Our system features a newly designed 16-DoF tendon-driven hand, equipped with wide angle wrist cameras and mounted on a Franka Emika Panda arm. We develop a versatile teleoperation pipeline and data collection protocol using both glove-based and VR interfaces, enabling high-quality data collection across diverse tasks such as pick and place, item sorting and assembly insertion. Leveraging high-frequency generative control, we train end-to-end policies from raw sensory inputs, enabling smooth, self-correcting motions in complex manipulation scenarios. Real-world evaluations demonstrate up to 93.3% out of distribution success rates, with up to a +33.3% performance boost due to emergent self-correcting behaviors, while also revealing scaling trends in policy performance. Our results advance the state-of-the-art in dexterous robotic manipulation through a fully integrated, practical approach to hardware, learning, and real-world deployment.
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。
