AI Technology & Industry Review
Berkeley Artificial Intelligence Research (BAIR) has introduced a new reinforcement learning (RL) method, Stochastic Optimal Control with Latent Representations (SOLAR), which can help robots quickly learn tasks such as stacking blocks or pushing objects from visual inputs.
The DeepMimic paper’s first author, Berkeley PhD student Xue Bin Peng, has now open-sourced the project’s codes, data, and frameworks. Moreover, Peng’s new research demonstrates that DeepMimic’s simulated characters can also learn to perform highly dynamic movements by using regular video clips of human examples as input data.
Electrifying an entire dance club is easy if you have killer moves like John Travolta in Saturday Night Fever. But for the rest of us, not so much. We may shake our butts and swing our arms, but let’s face it: some people just can’t dance. But now there’s hope, thanks to AI.
UC Berkeley’s Artificial Intelligence Research Lab (BAIR) has open-sourced their newest driving database, BDD100K, which contains over 100k videos of driving experience, each running 40 seconds at 30 frames per second.
The Berkeley Artificial Intelligence Research (BAIR) Lab yesterday proposed DeepMimic, a Reinforcement Learning (RL) technique that enables simulated characters to regenerate highly dynamic physical movements learned from data collected from human subjects.
Engineers have long struggled to teach robots how to perform actions that appear simple by human standards. Former OpenAI research scientist Peter Chen believes the solution is transforming such robots into teachable apprentices.