CoRL 2026 Poster 🌟 A team from UC San Diego developed HuG, a framework for humanoid robot grasping. Their goal: Enable humanoid robots to find objects on their own and grasp them stably through coordinated whole-body movements, without relying on real-world grasping demonstrations. They break the task into three stages: Perceive: Find a viewpoint that reveals the target. Align: Position the whole body for a feasible grasp. Grasp: Reach out and pick up the object. The key is how HuG learns which grasps are actually executable. The team put the same whole-body controller used on the real robot into the simulation loop, filtering out grasps it couldn't reliably execute. 73% of grasps that were kinematically valid failed this test. HuG ran on a Unitree G1, trained entirely in simulation with zero real-world demonstrations. The results: • 87.5% success on a real-world multi-level shelf task. • 85% on objects unseen during training. • 73%+ across all five tested table heights in a separate experiment.