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2026-02-14 19:06:02
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RoboNet Abstract Code and Sundry Featured Robots By the Numbers Contribute RoboNet Sudeep Dasari 1,4 , Frederik Ebert 1 , Stephen Tian 1 , Suraj Nair 2 , Bernadette Bucher 3 , Karl Schmeckpeper 3 , Siddharth Singh 3 , Sergey Levine 1 , Chelsea Finn 2 UC Berkeley 1 , Stanford University 2 , University of Pennsylvania 3 , CMU 4 Find Out More Abstract Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the promise of providing effective generalization to a wide range of open-world environments. However, these same methods typically require large amounts of diverse training data to generalize effectively. In contrast, most robotic learning experiments are small-scale, single-domain, and single-robot. This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically lar...
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