AlphaNIST
Summary
AlphaNIST applies reinforcement learning to contact-rich robotic assembly in simulation, learning end-to-end policies from binary rewards without human demonstrations. The work demonstrates emergent assembly skills on a NIST-inspired task board, using diverse starting states and success-guided sampling to improve training efficiency. A paper is available on arXiv with a code release planned.