Manipulation

TossingBot

TossingBot is a 2019 system from Princeton and Google in which a robot arm learned to grasp arbitrary objects from clutter and throw them accurately into target bins, achieving pick rates beyond what placing allows. Its key idea, residual physics, combined an analytical ballistic model with a learned network that predicts per-object corrections to release velocity, trained from trial and error. It became a landmark for dynamic manipulation and hybrid model-plus-learning control.

Why it matters for physical AI

Exploiting dynamics rather than avoiding them can multiply robot throughput, and TossingBot's residual-physics recipe remains a template for combining known physical models with learned corrections in deployed systems.