Tilde: Teleoperation for Dexterous In-Hand Manipulation Learning with a DeltaHand
Zilin Si*, Kevin Zhang*, Zeynep Temel, Oliver Kroemer
The Robotics Institute, Carnegie Mellon University
📎 Paper 📎 Codes 📎 Design files
Overview
Tilde is 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.
Design
All design files can be found here.
DeltaHand
Please refer to the design from DELTAHANDS .
TeleHand
Overview of all the components used for making a TeleHand.
Tutorial for assembling a TeleHand.
BibTex
@article{si2024tilde,
title={Tilde: Teleoperation for Dexterous In-Hand Manipulation Learning with a DeltaHand},
author={Si, Zilin and Zhang, Kevin Lee and Temel, Zeynep and Kroemer, Oliver},
journal={arXiv preprint arXiv:2405.18804},
year={2024}
}
Contact: Zilin Si, zsi at andrew dot cmu dot edu