Cassie Robot Uses Reinforcement Learning to Teach Itself How to Walk
Researchers at UC Berkeley develop a two-legged robot named Cassie that learns to walk through reinforcement learning instead of traditional programming. This approach allows the machine to adapt to different terrains by practicing in a virtual simulation first.
Researchers at the University of California, Berkeley, create a bright yellow, human-sized robot named Cassie that teaches itself how to walk using a form of artificial intelligence called reinforcement learning. Instead of relying on traditional programming, the robot learns through trial and error, gradually figuring out how to balance and take steps without human intervention.
This innovative approach addresses a major challenge in robotics, as writing code for a two-legged robot to navigate various environments requires staggering amounts of programming. Traditional methods struggle to adapt to changing surfaces like rocky paths or slick floors, but reinforcement learning gives Cassie the robustness and versatility to handle diverse real-world conditions on its own.
Before Cassie takes physical steps, the researchers test its capabilities extensively in a virtual simulation to ensure it is ready for the real world. This method stands in contrast to the highly choreographed routines often showcased by companies like Boston Dynamics, highlighting that reliable, adaptable bipedal locomotion remains a complex problem that requires self-teaching algorithms to solve effectively.