  We propose a novel fast and accurate simulation framework for
contact-intensive tight-tolerance robotic assembly tasks. The key components of
our framework are as follows: 1) data-driven contact point clustering with a
certain variable-input network, which is explicitly trained for simulation
accuracy (with real experimental data) and able to accommodate
complex/non-convex object shapes; 2) contact force solving, which
precisely/robustly enforces physics of contact (i.e., no penetration, Coulomb
friction, maximum energy dissipation) with contact mechanics of contact nodes
augmented with that of their object; 3) contact detection with a neural
network, which is parallelized for each contact point, thus, can be computed
very quickly even for complex shape objects with no exhaust pair-wise test; and
4) time integration with PMI (passive mid-point integration), whose
discrete-time passivity improves overall simulation accuracy, stability, and
speed. We then implement our proposed framework for two
widely-encountered/benchmarked contact-intensive tight-tolerance tasks, namely,
peg-in-hole assembly and bolt-nut assembly, and validate its speed and accuracy
against real experimental data. It is worthwhile to mention that our proposed
simulation framework is applicable to other general contact-intensive
tight-tolerance robotic assembly tasks as well. We also compare its performance
with other physics engines and manifest its robustness via haptic rendering of
virtual bolting task.
