SLAC: Safe and Efficient Real-Robot Reinforcement Learning

SIGNAL ANALYSIS

Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom systems. SLAC addresses this challenge with unsupervised simulation pre-training for safe and efficient real-robot reinforcement learning. This makes obsolete traditional trial-and-error methods in robotics, predicting adoption of simulation-based training pipelines in industrial settings.