《Science Robotics》重磅：仅需2小时，机器人柔性装配技能直逼人类顶尖水平

Core Insights - The article discusses the challenges in robotic manipulation and introduces a new system called HIL-SERL that significantly improves the efficiency and effectiveness of robotic training in real-world scenarios [1][2]. Traditional Methods Challenges - Traditional robotic control methods require extensive engineering design or imitation learning, which often lack adaptability and struggle in new environments [1]. - These methods fail to achieve human-level proficiency and speed, leading to inefficiencies in real-world applications [1]. HIL-SERL System Overview - The HIL-SERL system developed by a research team at UC Berkeley allows robots to learn complex tasks with only 1 to 2.5 hours of real-world training, achieving near-perfect success rates and surpassing human execution speeds [2][3]. - The system combines human guidance with autonomous exploration, creating an efficient and safe learning loop [3]. System Architecture - HIL-SERL consists of three core components: an executor process, a learner process, and a replay buffer integrated within the learner [4]. - It employs off-policy reinforcement learning techniques to optimize behavior strategies by leveraging historical data, allowing robots to learn from human demonstrations and assess the contribution of different actions towards achieving goals [4]. Performance in Multi-Task Scenarios - The system was tested on challenging tasks such as precision assembly, dual-arm coordination, and dynamic manipulation, demonstrating its versatility [5][8]. - In precision assembly tasks, robots achieved sub-millimeter accuracy, while in dual-arm coordination tasks, they effectively managed complex operations requiring synchronized movements [8]. Results and Adaptability - After 1 to 2.5 hours of training, robots achieved nearly 100% success rates and executed tasks 1.8 times faster than traditional imitation learning methods, which had an average success rate of 49.7% [9]. - The robots exhibited remarkable adaptability, successfully adjusting to unexpected situations, such as misalignments or disturbances, showcasing their ability to learn from real-time feedback [12]. Learning Mechanism - HIL-SERL's adaptability stems from its ability to evolve different control strategies based on task requirements, allowing for real-time adjustments and corrections [13][16]. - For high-precision tasks, the system employs a closed-loop response strategy, while for dynamic tasks, it utilizes an open-loop predictive strategy, demonstrating a high level of confidence in executing planned actions [13]. Conclusion - The research highlights the potential of HIL-SERL to overcome traditional reinforcement learning limitations, enabling efficient learning of complex skills in real-world environments [14]. - This advancement opens new avenues for industrial applications, particularly in flexible manufacturing sectors requiring small-batch production [14].