SenseAct™ is a benchmark task suite for developing and evaluating reinforcement learning methods with physical robots by abstracting over the complexity of real-time control of robotic components. SenseAct’s guiding principles of minimizing delays and maximizing timing consistency via proactive computation lead to responsive learned behavior and reliable learning via state-of-the-art algorithms. All the task implementations share a core structure which can be reused to implement new robotic tasks that benefit from SenseAct’s tight control over system delays.

SenseAct is an important new step in machine learning research on robots, enabling consistent and reproducible results on physical robots for the first time. I am particularly impressed by the attention that has been paid to efficient implementation minimizing the delays in the real-time learning system. I plan to adopt SenseAct in some of my own research at the University of Alberta. SenseAct will establish a standard that may greatly accelerates machine learning research on physical robots, pushing reinforcement learning to a new level of performance just as large standard data sets have for supervised learning.
— Richard S. Sutton, professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at University of Alberta

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