The editor of Downcodes learned that Sergey Levine’s research team at the BAIR Laboratory at the University of California, Berkeley, successfully developed a reinforcement learning framework called HIL-SERL, which aims to break through the bottleneck of robots learning complex operating skills, especially in real-world environments. . This technology combines human demonstration and correction, and applies efficient reinforcement learning algorithms, allowing robots to master a variety of precise operations in a very short time, significantly improving efficiency and providing a new direction for the future development of the field of robotics.
This new technology combines human demonstration and correction with efficient reinforcement learning algorithms, enabling robots to master a variety of precision and dexterity operating tasks, such as dynamic manipulation, precision assembly, and dual-arm collaboration, in just 1 to 2.5 hours.
In the past, it was difficult for robots to learn new skills. It was like teaching a naughty child to write homework. It had to be taught step by step and corrected over and over again. What is even more troublesome is that in the real world, various situations are complex and changeable, and robots often learn slowly, forget quickly, and overturn accidentally.
After a series of experiments, the effect of HIL-SERL is amazing. In various tasks, the robot has achieved a success rate of nearly 100% in just 1 to 2.5 hours, and the operation speed is nearly 2 times faster than before.
More importantly, HIL-SERL is the first system to use reinforcement learning to achieve dual-arm coordination based on image input in the real world, that is, it can allow two robot arms to work together to complete more complex tasks, For example, assembling timing belts is an operation that requires a high degree of coordination.
The emergence of HIL-SERL not only allows us to see the huge potential of robot learning, but also points out the direction for future industrial applications and research. Maybe, in the future, each of us will have such a robot "apprentice" at home, helping us with housework, assembling furniture, and even playing games with us. It feels cool to think about it!
Of course, HIL-SERL also has some limitations. For example, it may not be able to handle tasks that require long-term planning. In addition, HIL-SERL is currently mainly tested in laboratory environments and has not been verified in large-scale real-world scenarios. However, I believe that with the advancement of technology, these problems will gradually be solved.
Paper address: https://hil-serl.github.io/static/hil-serl-paper.pdf
Project address: https://hil-serl.github.io/
The breakthrough progress of the HIL-SERL framework has brought new hope to the development of robotics technology, and its application prospects in the real world are broad. Although there are still some limitations at present, we believe that with continued research and improvement, HIL-SERL will play a greater role in the future and bring more convenience to people's lives.