人形机器人新突破！敏捷稳定两不误

Core Idea - The article discusses the AMS (Agility Meets Stability) framework developed by a joint research team from the University of Hong Kong, NVIDIA, and Tsinghua University, which successfully integrates dynamic motion tracking and extreme balance control in humanoid robots using a single strategy [3]. Group 1: Key Innovations of AMS - Heterogeneous Data Sources: AMS generates scalable balance data by directly sampling from the robot's action space, overcoming human data limitations and alleviating long-tail distribution issues [2][17]. - Hybrid Reward Mechanism: AMS employs selective application of balance prior rewards to provide precise balance guidance without sacrificing agility, resolving conflicts in optimization objectives [4][21]. - Adaptive Learning Strategy: The framework dynamically adjusts sampling probabilities and tailors learning for each action, enabling efficient adaptive learning [4][23]. Group 2: Challenges in Humanoid Robotics - Humanoid robots face a dilemma of needing both agile dynamic movement and precise balance control to perform tasks in human environments [5][6]. - Existing research primarily focuses on either dynamic motion tracking or balance control, making it difficult to achieve both capabilities within a unified framework [8][10]. Group 3: Experimental Results - The AMS framework was validated on the Unitree G1 humanoid robot, demonstrating excellent performance in dynamic motion tracking, including activities like shuttle runs and basketball dribbling [24]. - AMS also showcased precise balance control capabilities, effectively managing extreme balance poses [26]. - The framework supports various real-time teleoperation modes, highlighting its practical value as a foundational control model [29]. Group 4: Conclusion - AMS represents a significant advancement in humanoid robot control, combining heterogeneous data sources, a hybrid reward mechanism, and an adaptive learning strategy to achieve both dynamic agility and robust balance, laying a crucial foundation for humanoid robots in human environments [33].