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

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

Text

Li et al. "CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks." Proceedings of The 9th Conference on Robot Learning, 2025.

Markdown

[Li et al. "CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks." Proceedings of The 9th Conference on Robot Learning, 2025.](https://mlanthology.org/corl/2025/li2025corl-clone/)

BibTeX

@inproceedings{li2025corl-clone,
  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},
  year      = {2025},
  pages     = {4493-4505},
  volume    = {305},
  url       = {https://mlanthology.org/corl/2025/li2025corl-clone/}
}