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[2602.15060] CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation
Summary
The paper presents CLOT, a closed-loop system for humanoid teleoperation that addresses global pose drift, enabling stable and precise long-duration motions.
Why It Matters
CLOT represents a significant advancement in humanoid robotics, addressing a critical challenge of pose drift in teleoperation. By enhancing the synchronization between human operators and robots, this research could improve applications in remote operations, rehabilitation, and interactive robotics, making them more reliable and effective.
Key Takeaways
- CLOT achieves closed-loop global motion tracking for humanoid teleoperation.
- The system mitigates pose drift through high-frequency localization feedback.
- A data-driven randomization strategy enhances stability during motion corrections.
- The approach utilizes a transformer-based policy trained on extensive human motion data.
- Real-world experiments demonstrate high precision and robustness in teleoperation.
Computer Science > Robotics arXiv:2602.15060 (cs) [Submitted on 13 Feb 2026] Title:CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation Authors:Tengjie Zhu, Guanyu Cai, Yang Zhaohui, Guanzhu Ren, Haohui Xie, ZiRui Wang, Junsong Wu, Jingbo Wang, Xiaokang Yang, Yao Mu, Yichao Yan, Yichao Yan View a PDF of the paper titled CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation, by Tengjie Zhu and 11 other authors View PDF HTML (experimental) Abstract:Long-horizon whole-body humanoid teleoperation remains challenging due to accumulated global pose drift, particularly on full-sized humanoids. Although recent learning-based tracking methods enable agile and coordinated motions, they typically operate in the robot's local frame and neglect global pose feedback, leading to drift and instability during extended execution. In this work, we present CLOT, a real-time whole-body humanoid teleoperation system that achieves closed-loop global motion tracking via high-frequency localization feedback. CLOT synchronizes operator and robot poses in a closed loop, enabling drift-free human-to-humanoid mimicry over long timehorizons. However, directly imposing global tracking rewards in reinforcement learning, often results in aggressive and brittle corrections. To address this, we propose a data-driven randomization strategy that decouples observation trajectories from reward evaluation, enabling smooth and stable global corrections. We furthe...
