Robots handle grip force and dexterity through a combination of sensors, control algorithms, and mechanical design. Grip force is managed using force/torque sensors embedded in the gripper or fingertips, which provide real-time feedback to adjust the applied pressure. For example, a robot picking up a fragile object like an egg might use a closed-loop control system that continuously measures the force exerted and reduces it if slippage is detected. Dexterity, on the other hand, relies on multi-jointed grippers or soft robotic materials that conform to an object’s shape. Advanced grippers, like those with articulated fingers, can perform precise tasks such as threading a needle or rotating a screwdriver by mimicking human hand movements.
The integration of tactile sensing and vision systems is critical for balancing force and precision. Tactile sensors, such as piezoresistive or capacitive arrays, map pressure distribution across the gripper’s surface, allowing the robot to detect subtle changes in contact. Vision systems, like depth cameras or stereo vision, help the robot identify an object’s position and orientation before grasping. For instance, a warehouse robot might use a 3D camera to locate a box, estimate its weight based on size, and then adjust its grip force to avoid crushing the contents. Machine learning models trained on object datasets further improve adaptability, enabling robots to generalize grip strategies for unfamiliar items.
Challenges remain in handling highly variable objects or dynamic environments. Solutions like variable-stiffness actuators (VSAs) allow grippers to switch between rigid and compliant modes—firm for heavy tools, soft for delicate produce. Collaborative robots (cobots) often incorporate safety-focused force limits to prevent harm to humans while maintaining grip stability. For example, a cobot in a manufacturing line might use torque-controlled motors to sense when a human operator is guiding its arm, reducing grip force automatically. Ongoing research focuses on improving sensor resolution and control loop speeds to achieve human-like dexterity, but current systems already excel in structured tasks like assembly or packaging.