Octi Zhang

AlphaNIST trains a single reinforcement-learning policy across all 20 assembly tasks on the NIST Assembly Task Board 1 using only a binary success reward, with no demonstrations, progress shaping, scripted primitives, or per-task tuning. Prior learning-based methods typically solved one task at a time or relied on fixed sequences, staged initial poses, and dense reward guidance. In simulation, the shared policy reaches 98.2% overall training success and outperforms separately trained specialists on small-part tasks.

AlphaNIST trains a single reinforcement-learning policy across all 20 assembly tasks on the NIST Assembly Task Board 1 using only a binary success reward, with no demonstrations, progress shaping, scripted primitives, or per-task tuning. Prior learning-based methods typically solved one task at a time or relied on fixed sequences, staged initial poses, and dense reward guidance. In simulation, the shared policy reaches 98.2% overall training success and outperforms separately trained specialists on small-part tasks.