Force-Controlled Robotic Manipulation
Overview
Force-controlled robotic manipulation studies how robots use contact forces to perform tasks that require more than accurate positioning, such as inserting parts, handling fragile objects, wiping surfaces, or turning valves. Common approaches include impedance and admittance control, hybrid force-position control, and tactile or force-feedback policies; newer systems pair slower vision or language-guided planning with fast reflexes that correct contact in real time. Researchers also use demonstrations, reinforcement learning, and simulation to learn force-aware behavior, sometimes estimating forces from vision, motor currents, or other low-cost signals instead of dedicated sensors. A central challenge is making these methods reliable across objects, tasks, and real-world conditions while balancing force accuracy, safety, sensing cost, and the speed of learned policies.
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