  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.
