Dexterous robotic manipulation remains a challenging domain due to its strict demands for precision and robustness on both hardware and software. While dexterous robotic hands have demonstrated remarkable capabilities in complex tasks, efficiently learning adaptive control policies for hands still presents a significant hurdle given the high dimensionalities of hands and tasks. To bridge this gap, we propose Tilde, an imitation learning-based in-hand manipulation system on a dexterous DeltaHand. It leverages 1) a low-cost, configurable, simple-to-control, soft dexterous robotic hand, DeltaHand, 2) a user-friendly, precise, real-time teleoperation interface, TeleHand, and 3) an efficient and generalizable imitation learning approach with diffusion policies. Our proposed TeleHand has a kinematic twin design to the DeltaHand that enables precise one-to-one joint control of the DeltaHand during teleoperation. This facilitates efficient high-quality data collection of human demonstrations in the real world. To evaluate the effectiveness of our system, we demonstrate the fully autonomous closed-loop deployment of diffusion policies learned from demonstrations across seven dexterous manipulation tasks with an average 90% success rate.

翻译：灵巧机器人操作因其对硬件和软件的精度与鲁棒性要求严格，仍然是一个具有挑战性的领域。尽管灵巧机器人手在复杂任务中已展现出卓越能力，但鉴于手部与任务的高维度特性，高效学习自适应控制策略仍面临重大障碍。为弥合这一差距，我们提出了Tilde——一种基于模仿学习的灵巧DeltaHand手内操作系统。该系统整合了三大核心组件：1）低成本、可配置、易于控制的软体灵巧机器人手DeltaHand；2）用户友好、精确、实时的遥操作接口TeleHand；3）采用扩散策略的高效可泛化模仿学习方法。我们设计的TeleHand与DeltaHand具有运动学孪生结构，可在遥操作期间实现对DeltaHand关节的一对一精确控制，从而高效采集现实世界中人类演示的高质量数据。为评估系统效能，我们在七项灵巧操作任务中展示了基于演示数据学习的扩散策略的完全自主闭环部署，平均成功率高达90%。