Object Deformation Suppression for Grasping Leveraging Optical Proximity Sensors

Grasping soft and individualized food and agri- cultural products without causing damage is a significant challenge in robotics. This task requires balancing two con- flicting demands: applying sufficient force to lift the object and avoiding excessive force that could cause damage. Conventional approaches include learning-based manipulation and sequential control based on slip detection. However, the former requires prior training, while the latter takes time to adjust the grasping force. Therefore, these methods are not suitable for environ- ments where object properties change frequently or for high- throughput operations. To address these issues, we propose a parameter adaptation method for deformation suppression that does not require learning and enables high-speed processing. The proposed method reduces the grasping force according to object deformation, which is detected by optical proximity sensors. A key benefit of optical proximity sensors is high- speed data acquisition, which enables real-time adjustment of grasping force without stopping the motion, leading to faster task completion. Furthermore, the deformation information obtained from the proximity sensor is converted into a virtual force, and the grasping force is adjusted based on the virtual dynamics framework. This enables seamless integration with pre-grasp control strategies that gently approach the object.