AlphaNIST

AlphaNIST applies reinforcement learning to teach robots complex assembly tasks by learning directly from experience rather than following prescribed instructions. The system trains a single policy across twenty diverse NIST assembly tasks with tight tolerances and small parts, using varied starting states and success-guided sampling to discover robot-appropriate solutions. The approach demonstrates that one generalized policy can outperform twenty specialized policies trained on individual tasks.