卢宗青团队新作：人类先验打底，统一动作对齐，通用机器人模型正在落地

Core Insights - The article discusses the challenges and advancements in the robotics industry, particularly focusing on the transition from machine learning models to real-world applications, emphasizing the importance of stability and reliability in deployment [2][3][31]. Group 1: Structural Challenges in Robotics - The robotics industry faces three structural barriers: morphological fragmentation, data cost and coverage, and deployment system issues, which hinder the generalization of robotic strategies [2][3]. - Morphological fragmentation leads to difficulties in data sharing among different robotic forms, requiring retraining when switching platforms [2]. - The high cost and scarcity of real-world robotic data limit the ability to cover long-range tasks and complex interactions, making cross-morphology generalization challenging [2][3]. Group 2: Research and Development of Being-H0.5 - The research team led by Lu Zongqing introduced the Being-H0.5 model, which aims to create a more stable and generalizable robotic control strategy by leveraging human-centric data and addressing the aforementioned barriers [3][4]. - The model utilizes a unified state-action space to overcome inconsistencies in action definitions across different hardware, facilitating knowledge sharing and transfer [4][24]. - The UniHand-2.0 dataset, comprising over 35,000 hours of data, serves as a foundation for training, integrating human hand operation data and robotic manipulation data to enhance the model's performance [13][23]. Group 3: Experimental Results and Performance - Experimental results indicate that while specialist models perform slightly better, the generalist Being-H0.5 model shows comparable performance, particularly in long-horizon and bimanual tasks, which are critical for assessing deployment stability [9][10]. - In real robot experiments, the generalist model demonstrated significant improvements in long-horizon and bimanual tasks, highlighting its potential for stable deployment in complex environments [9][10]. - The model achieved an average success rate of 98.9% on the LIBERO benchmark and 53.9% on the RoboCasa benchmark, showcasing its robustness in both simulated and real-world scenarios [14][15]. Group 4: Deployment Mechanisms and Stability - The introduction of mechanisms like MPG (Motion Policy Generation) and UAC (Unified Action Control) is crucial for ensuring the stability of the model during deployment, particularly for long-range and bimanual tasks [17][18]. - The absence of these mechanisms leads to significant performance degradation, emphasizing the importance of stability in real-world applications [17][18]. - The research highlights that achieving reliable deployment requires addressing both action distribution constraints and asynchronous control issues [33]. Group 5: Implications for Future Robotics - The findings suggest that cross-morphology unified action learning is feasible, allowing multiple robots to share the same strategy without extensive retraining [30]. - Human hand video and action data are essential for developing generalist models, providing a natural action prior that enhances generalization and transferability across different robotic forms [30]. - The work underscores the need for a comprehensive approach that integrates data, alignment, generation, and deployment stability to advance the field of general robotic intelligence [30].