TOP: Time Optimization Policy for Stable and Accurate Standing Manipulation with Humanoid Robots

2025-08-01

AI Summary

Bipedal humanoid robots struggle to simultaneously maintain balance, achieve motion accuracy, and ensure control efficiency during high-speed upper-body manipulation. To address this, we propose the Time-Optimized Policy (TOP), a hierarchical control framework that decouples upper- and lower-body control via joint optimization of temporal trajectory and joint configuration for upper-body motions. The upper body employs a VAE-encoded motion prior integrated with a PD controller for high-fidelity trajectory tracking, while the lower body utilizes a reinforcement learning–based robust balance controller to reduce sensitivity to disturbances. This architecture significantly improves inter-limb coordination and alleviates lower-body control burden. Evaluated in simulation and on a physical humanoid platform, TOP reduces pose error by 32% and instability rate by 76% in rapid upright manipulation tasks, while maintaining real-time performance. Our key contribution is the first explicit incorporation of the time dimension into a unified optimization framework for coordinated manipulation and balance control in humanoid robotics.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Humanoid robots have the potential capability to perform a diverse range of manipulation tasks, but this is based on a robust and precise standing controller. Existing methods are either ill-suited to precisely control high-dimensional upper-body joints, or difficult to ensure both robustness and accuracy, especially when upper-body motions are fast. This paper proposes a novel time optimization policy (TOP), to train a standing manipulation control model that ensures balance, precision, and time efficiency simultaneously, with the idea of adjusting the time trajectory of upper-body motions but not only strengthening the disturbance resistance of the lower-body. Our approach consists of three parts. Firstly, we utilize motion prior to represent upper-body motions to enhance the coordination ability between the upper and lower-body by training a variational autoencoder (VAE). Then we decouple the whole-body control into an upper-body PD controller for precision and a lower-body RL controller to enhance robust stability. Finally, we train TOP method in conjunction with the decoupled controller and VAE to reduce the balance burden resulting from fast upper-body motions that would destabilize the robot and exceed the capabilities of the lower-body RL policy. The effectiveness of the proposed approach is evaluated via both simulation and real world experiments, which demonstrate the superiority on standing manipulation tasks stably and accurately. The project page can be found at https://anonymous.4open.science/w/top-258F/.

Problem

• Ensuring balance and precision in humanoid robot standing manipulation
• Optimizing time efficiency for fast upper-body motions without destabilizing
• Decoupling upper-body precision control and lower-body stability reinforcement

Innovation

• Time optimization policy for stable standing manipulation
• Decoupled upper-body PD and lower-body RL control
• VAE-enhanced upper-lower body motion coordination