Contact-Rich Robotic Manipulation

This field studies how robots make and maintain useful contact with objects, where small errors in force, alignment, or friction can cause failure or damage. Approaches include tactile- and force-aware imitation learning and reinforcement learning, compliant and hybrid force-position control, contact-dynamics models, and planning methods that reason over contact modes. Recent work also brings these capabilities into vision-language-action policies and predictive world models, often using tactile feedback for fast corrections while vision guides the broader task. A central challenge is making policies safe and reliable across unseen objects, sensors, and real-world contact conditions, since more sensing or higher simulation fidelity does not consistently improve every task.