TY - GEN
T1 - Holo-Dex
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
AU - Arunachalam, Sridhar Pandian
AU - Guzey, Irmak
AU - Chintala, Soumith
AU - Pinto, Lerrel
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we present Holo - Dex, a framework for dexter-ous manipulation that places a teacher in an immersive mixed reality through commodity VR headsets. The high-fidelity hand pose estimator onboard the headset is used to teleoperate the robot and collect demonstrations for a variety of general-purpose dexterous tasks. Given these demonstrations, we use powerful feature learning combined with non-parametric imi-tation to train dexterous skills. Our experiments on six common dexterous tasks, including in-hand rotation, spinning, and bottle opening, indicate that HOLO-DEX can both collect high-quality demonstration data and train skills in a matter of hours. Finally, we find that our trained skills can exhibit generalization on objects not seen in training. Videos of HOLO - DEX are available on {https://holo-dex.github.io/.}
AB - A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we present Holo - Dex, a framework for dexter-ous manipulation that places a teacher in an immersive mixed reality through commodity VR headsets. The high-fidelity hand pose estimator onboard the headset is used to teleoperate the robot and collect demonstrations for a variety of general-purpose dexterous tasks. Given these demonstrations, we use powerful feature learning combined with non-parametric imi-tation to train dexterous skills. Our experiments on six common dexterous tasks, including in-hand rotation, spinning, and bottle opening, indicate that HOLO-DEX can both collect high-quality demonstration data and train skills in a matter of hours. Finally, we find that our trained skills can exhibit generalization on objects not seen in training. Videos of HOLO - DEX are available on {https://holo-dex.github.io/.}
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U2 - 10.1109/ICRA48891.2023.10160547
DO - 10.1109/ICRA48891.2023.10160547
M3 - Conference contribution
AN - SCOPUS:85168674206
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5962
EP - 5969
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2023 through 2 June 2023
ER -