Robotic Control

Robotic control turns sensor readings and task goals into actions, from steering mobile robots to coordinating humanoid bodies and handling delicate contact. Methods range from feedback control, inverse kinematics, and model predictive control to reinforcement learning, imitation learning, and vision-language-action policies. Current work often combines these approaches, adding force or tactile feedback, safety constraints, and sim-to-real adaptation to improve performance on manipulation, locomotion, and human-robot interaction tasks. A central challenge is achieving safe, stable behavior that generalizes to unfamiliar conditions while meeting real-time limits and retaining task performance.