Robots are learning to push with precision... Researchers from ETH Zurich have developed a learning-based controller that enables a quadrupedal robot with an arm to push and reorient unknown objects with high accuracy. Using constrained reinforcement learning, the system adapts to different object properties—mass, material, size, and shape—while achieving a 91.35% success rate in simulation and over 80% on hardware. The robot dynamically adjusts its pushing strategy, ensuring stable and contact-rich interactions without prior object knowledge.