Development of a 3axis Human Fingertip Tactile Sensor with an OrthoPlanar Spring Human experts have mastered many manual skills. Transferring those skills to robots or human novices is challenging. Not only the movements, but also the exerted forces are of importance. In this paper we propose a sensor that can measure the force vector acting on the human fingertip, without covering the part of the fingertip that is contact with the touched object. Compared to our previous iterations of this sensor, we use a spring instead of viscoelastic material as the deformable material for the sensor. While it is challenging to include a spring in the given form factor, springs have the benefit of lower hysteresis than viscoelastic materials. This paper presents the integration of the spring in the sensor and shows experimental validation with 8 subjects. Compared to our previous version of the sensor with 2 Hall effect sensors, we achieved improved sensing characteristics.

Experimental study on model vs. learningbased slip detection Vision and proprioception are traditional sources of information for robotic grasping, but they are insufficient to achieve a stable grasp without slippage or without applying an excessive force on the object. Tactile sensors can aid in this problem by providing spatial and temporal data on the contact between fingertips and object. In this work, tactile fingertip sensors are used to detect slippage through two separate methods: the first, using principles inspired by human tactile sensing, and the second, by using a convolutional neural network trained with suitably labeled test samples. To perform a fair comparison of the methods, two evaluations are performed using a test bench and a pickandplace robotic application. Results show promising use of the modelbased method to avoid translational slippage, as it was able to consistently keep objects from slipping without overloading the grasp. Limitations of both model and learningbased approaches are identified and discussed.