Humanoid robots can learn coordinated, anticipatory interaction with humans by training on diverse motion-capture data adapted to the robot's morphology, enabling real-world physical collaboration without explicit coordination rules.
This paper presents Social Humanoid, a robot system that learns to physically interact and coordinate with humans in real-time. Using a two-level approach—a high-level motion planner and low-level controller—the system predicts and responds to human movements, even when the human is partially hidden.