CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation
arXiv:2602.15060v1 Announce Type: cross 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 s
arXiv:2602.15060v1 Announce Type: cross 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 further regularize the policy with an adversarial motion prior to suppress unnatural behaviors. To support CLOT, we collect 20 hours of carefully curated human motion data for training the humanoid teleoperation policy. We design a transformer-based policy and train it for over 1300 GPU hours. The policy is deployed on a full-sized humanoid with 31 DoF (excluding hands). Both simulation and real-world experiments verify high-dynamic motion, high-precision tracking, and strong robustness in sim-to-real humanoid teleoperation. Motion data, demos and code can be found in our website.
Executive Summary
The article introduces CLOT, a real-time whole-body humanoid teleoperation system designed to address the challenge of global pose drift in long-horizon teleoperation tasks. CLOT achieves closed-loop global motion tracking through high-frequency localization feedback, synchronizing operator and robot poses to enable drift-free mimicry over extended periods. The system employs a data-driven randomization strategy to decouple observation trajectories from reward evaluation, ensuring smooth and stable global corrections. Additionally, an adversarial motion prior is used to regularize the policy and suppress unnatural behaviors. The policy is trained using a transformer-based architecture and extensive human motion data, demonstrating high-dynamic motion, high-precision tracking, and strong robustness in both simulation and real-world experiments.
Key Points
- ▸ CLOT addresses the issue of global pose drift in humanoid teleoperation.
- ▸ The system uses high-frequency localization feedback for closed-loop global motion tracking.
- ▸ A data-driven randomization strategy ensures smooth and stable global corrections.
- ▸ An adversarial motion prior is employed to regularize the policy and suppress unnatural behaviors.
- ▸ The policy is trained using a transformer-based architecture and extensive human motion data.
Merits
Innovative Approach
CLOT presents a novel approach to addressing the long-standing issue of global pose drift in humanoid teleoperation, leveraging high-frequency localization feedback for closed-loop tracking.
Robust Performance
The system demonstrates high-dynamic motion, high-precision tracking, and strong robustness in both simulation and real-world experiments, indicating its practical applicability.
Comprehensive Training
The use of a transformer-based policy trained on extensive human motion data ensures the system's ability to handle complex and varied teleoperation tasks.
Demerits
Complexity
The system's reliance on high-frequency localization feedback and extensive training data may pose challenges in terms of computational resources and implementation complexity.
Generalizability
While the system shows promise, its performance in diverse and unstructured environments remains to be thoroughly tested, raising questions about its generalizability.
Ethical Considerations
The use of humanoid teleoperation raises ethical considerations, particularly regarding the potential for misuse or the impact on human labor markets.
Expert Commentary
The article presents a significant advancement in the field of humanoid teleoperation, addressing a critical challenge in global pose drift through innovative closed-loop tracking mechanisms. The use of high-frequency localization feedback and data-driven randomization strategies demonstrates a sophisticated approach to ensuring stability and precision in teleoperation tasks. The transformer-based policy, trained on extensive human motion data, further enhances the system's robustness and adaptability. However, the complexity of the system and the ethical considerations surrounding humanoid teleoperation cannot be overlooked. Future research should focus on simplifying the implementation process and addressing the ethical implications to ensure the responsible deployment of such technologies. The potential applications of CLOT are vast, ranging from industrial automation to disaster response, making it a valuable contribution to the field of robotics and AI.
Recommendations
- ✓ Further research should explore the generalizability of CLOT in diverse and unstructured environments to ensure its robustness across different scenarios.
- ✓ Ethical guidelines and regulatory frameworks should be developed in parallel with technological advancements to address the potential implications of humanoid teleoperation on society and labor markets.
