Boston Dynamics’ robots, though appearing highly agile in videos, are still manually coded and struggle with new obstacles. However, researchers have used reinforcement learning to teach a robot, Cassie, dynamic movements without explicit training. This approach enables rapid skill acquisition, with Cassie successfully running 400 meters and performing high jumps. Further studies will explore adapting the technique for robots with cameras.
Teaching Robots to Move with AI
If you’ve seen Boston Dynamics’ videos of robots performing impressive feats, you might think they’ve learned to be incredibly agile. However, these robots are still manually programmed and struggle with new obstacles.
A new method using AI, called reinforcement learning, is changing this. Researchers taught a two-legged robot, Cassie, to run 400 meters and perform jumps over varying terrains without explicit training for each movement. This approach enables robots to adapt to new scenarios, similar to how humans learn and respond to unpredictable events.
How It Works
The team used reinforcement learning to reward or penalize the robot as it attempted to carry out specific tasks. This method taught the robot to generalize and respond in new scenarios, rather than freezing like previous models.
Using simulations, the robot was trained to master basic skills, such as jumping and running, and then encouraged to perform new tasks using its learned movements. The robot was also exposed to task randomization to prepare it for unexpected scenarios.
Practical Applications
This approach dramatically speeds up the learning process, allowing the robot to perform the same skills in the real world without further fine-tuning. For example, Cassie completed a 400-meter run in two minutes and 34 seconds and jumped 1.4 meters in the long jump without additional training.
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