Robot Learning

Robot learning studies how robots can turn observations and experience into actions, from precise manipulation and locomotion to navigation and human-robot interaction. Common approaches include imitation learning from human or robot demonstrations, reinforcement learning through interaction, model-based control, and vision-language-action models that connect instructions and images to actions. Researchers also use reusable skills, structured physical representations, tactile sensing, and simulation or world models to improve transfer across tasks, objects, and robot bodies. A central challenge is making policies reliable beyond their training data: larger and more varied datasets can help, but physical feasibility, sim-to-real transfer, long-horizon recovery, and evaluation that predicts real-world success remain open concerns.