CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks

Yixuan Li, Yutang Lin, Jieming Cui, Tengyu Liu, Wei Liang, Yixin Zhu, Siyuan Huang
Proceedings of The 9th Conference on Robot Learning, PMLR 305:4493-4505, 2025.

Abstract

Humanoid robot teleoperation plays a vital role in demonstrating and collecting data for complex interactions. Current methods suffer from two key limitations: (1) restricted controllability due to decoupled upper- and lower-body control, and (2) severe drift caused by open-loop execution. These issues prevent humanoid robots from performing coordinated whole-body motions required for long-horizon loco-manipulation tasks. We introduce CLONE, a whole-body teleoperation system that overcomes these challenges through three key contributions: (1) a Mixture-of-Experts (MoE) whole-body control policy that enables complex coordinated movements, such as “picking up an object from the ground” and “placing it in a distant bin”; (2) a closed-loop error correction mechanism using LiDAR odometry, reducing translational drift to 12cm over 8.9-meter trajectories; and (3) a systematic data augmentation strategy that ensures robust performance under diverse, previously unseen operator poses. In extensive experiments, CLONE demonstrates robust performance across diverse scenarios while maintaining stable whole-body control. These capabilities significantly advance humanoid robotics by enabling the collection of long-horizon interaction data and establishing a foundation for more sophisticated humanoid-environment interaction in both research and practical applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-li25h, title = {CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks}, author = {Li, Yixuan and Lin, Yutang and Cui, Jieming and Liu, Tengyu and Liang, Wei and Zhu, Yixin and Huang, Siyuan}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {4493--4505}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/li25h/li25h.pdf}, url = {https://proceedings.mlr.press/v305/li25h.html}, abstract = {Humanoid robot teleoperation plays a vital role in demonstrating and collecting data for complex interactions. Current methods suffer from two key limitations: (1) restricted controllability due to decoupled upper- and lower-body control, and (2) severe drift caused by open-loop execution. These issues prevent humanoid robots from performing coordinated whole-body motions required for long-horizon loco-manipulation tasks. We introduce CLONE, a whole-body teleoperation system that overcomes these challenges through three key contributions: (1) a Mixture-of-Experts (MoE) whole-body control policy that enables complex coordinated movements, such as “picking up an object from the ground” and “placing it in a distant bin”; (2) a closed-loop error correction mechanism using LiDAR odometry, reducing translational drift to 12cm over 8.9-meter trajectories; and (3) a systematic data augmentation strategy that ensures robust performance under diverse, previously unseen operator poses. In extensive experiments, CLONE demonstrates robust performance across diverse scenarios while maintaining stable whole-body control. These capabilities significantly advance humanoid robotics by enabling the collection of long-horizon interaction data and establishing a foundation for more sophisticated humanoid-environment interaction in both research and practical applications.} }
Endnote
%0 Conference Paper %T CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks %A Yixuan Li %A Yutang Lin %A Jieming Cui %A Tengyu Liu %A Wei Liang %A Yixin Zhu %A Siyuan Huang %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-li25h %I PMLR %P 4493--4505 %U https://proceedings.mlr.press/v305/li25h.html %V 305 %X Humanoid robot teleoperation plays a vital role in demonstrating and collecting data for complex interactions. Current methods suffer from two key limitations: (1) restricted controllability due to decoupled upper- and lower-body control, and (2) severe drift caused by open-loop execution. These issues prevent humanoid robots from performing coordinated whole-body motions required for long-horizon loco-manipulation tasks. We introduce CLONE, a whole-body teleoperation system that overcomes these challenges through three key contributions: (1) a Mixture-of-Experts (MoE) whole-body control policy that enables complex coordinated movements, such as “picking up an object from the ground” and “placing it in a distant bin”; (2) a closed-loop error correction mechanism using LiDAR odometry, reducing translational drift to 12cm over 8.9-meter trajectories; and (3) a systematic data augmentation strategy that ensures robust performance under diverse, previously unseen operator poses. In extensive experiments, CLONE demonstrates robust performance across diverse scenarios while maintaining stable whole-body control. These capabilities significantly advance humanoid robotics by enabling the collection of long-horizon interaction data and establishing a foundation for more sophisticated humanoid-environment interaction in both research and practical applications.
APA
Li, Y., Lin, Y., Cui, J., Liu, T., Liang, W., Zhu, Y. & Huang, S.. (2025). CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:4493-4505 Available from https://proceedings.mlr.press/v305/li25h.html.

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