Four-legged robot moves chairs with contact-seeking training, a post reports

The post describes a reinforcement-learning problem: when task rewards stay at zero until contact, a robot can get stuck optimizing smoothness and energy penalties instead. It credits researchers from the University of Pisa, ETH Zürich and Nvidia with adding a separate “critic,” or evaluator, trained on contact-seeking rewards. Its influence decreases during training, shifting emphasis toward task performance. According to the post, candidate contact points come from a general-purpose grasping algorithm. It reports tests on a real four-legged robot with a manipulator that moved chairs, transferred to unseen IKEA furniture without additional training, and recovered from failed contact attempts.