Deep Learning of Proprioceptive Models for Robotic Force Estimation Many robotic tasks require fast and accurate force sensing capabilities to ensure adaptive behavior execution. While dedicated forcetorque (FT) sensors are a common option, such devices induce extra costs, need additional power supply, and add weight to otherwise lightweight robotic systems. This paper presents a machine learning approach for estimating external forces acting on a robot based on common internal sensors only. In the training phase, a behaviorspecific proprioceptive model is learned as compact representation of the expected proprioceptive feedback during task execution. First, the proprioceptive sensors relevant for the given behavior are identified using informationtheoretic measures. Then, the proprioceptive model is learned using deep learning techniques. During behavior execution, the proprioceptive model is applied to actual sensor readings for estimation of external forces. Experiments performed with the UR5 robot demonstrate the ability for fast and accurate force estimation even in situations where a dedicated commercial FT sensor is not applicable.

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.