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DayDreamer algorithm enables robot learning without simulation

Organization: UC Berkeley
Country: USA
Website: berkeley.edu
Year Founded: 1868
Number of Employees: 500+

Description:
2023 RBR50 LogoUC Berkeley’s DayDreamer is a reinforcement-learning (RL) algorithm that, for example, taught a quadruped to walk in just one hour without interacting with a simulator. DayDreamer uses neural networks to interact with the environment, and then uses the information it gathers to learn a world model. This world model allows machine learning to predict the results of a series of actions. This predicted behavior is used with RL to train a controller for the robot.

Analysis:
The DayDreamer algorithm was successfully used to train a Unitree Robotics A1 Quadruped to roll off its back and walk in just an hour, a Universal Robot UR5 manipulator and a UFACTORY xArm 6 to complete a pick-and-place task in around 10 hours, and a Sphero Ollie mobile robot a navigation task in two hours. Berkeley’s algorithm works fast and is well equipped to handle the complexity and dynamics of the real world than a simulated environment. DayDreamer’s world model also requires less development time and cost than a simulated model. Overall, the DayDreamer algorithm could lead to big improvements in the amount of time and funding it takes to train robots to do complex tasks. – Brianna Wessling
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