  Robotic assembly for high-mixture settings requires adaptivity to diverse
parts and poses, which is an open challenge. Meanwhile, in other areas of
robotics, large models and sim-to-real have led to tremendous progress.
Inspired by such work, we present AutoMate, a learning framework and system
that consists of 4 parts: 1) a dataset of 100 assemblies compatible with
simulation and the real world, along with parallelized simulation environments
for policy learning, 2) a novel simulation-based approach for learning
specialist (i.e., part-specific) policies and generalist (i.e., unified)
assembly policies, 3) demonstrations of specialist policies that individually
solve 80 assemblies with 80% or higher success rates in simulation, as well as
a generalist policy that jointly solves 20 assemblies with an 80%+ success
rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better)
performance than simulation, including on perception-initialized assembly. The
key methodological takeaway is that a union of diverse algorithms from
manufacturing engineering, character animation, and time-series analysis
provides a generic and robust solution for a diverse range of robotic assembly
problems. To our knowledge, AutoMate provides the first simulation-based
framework for learning specialist and generalist policies over a wide range of
assemblies, as well as the first system demonstrating zero-shot sim-to-real
transfer over such a range. For videos and additional details, please see our
project website: https://bingjietang718.github.io/automate/
