Grip Force Perception Based on dAENN for Minimally Invasive Surgery Robot Although robot assisted minimally invasive surgery brings the gospel to patients, force perception is gone. Among all the contact forces during surgery, the instrument grip force plays the most important role. In this paper, a grip force perception method based on denoising AutoEncoder Neural Network (dAENN) is proposed. The method utilizes sensor data including encoder readings and motor current over a time window as the input of dAENN for sufficient information. An Artificial Neural Network (ANN) is then introduced as a machine learning tool to learn the nonlinear mapping between the compact features and grip force labels. Feature extraction is first introduced into grip force perception problem in this paper. Experiment results shows adequate expressive capability of the extracted coding as well as the superior grip force perception performance over several popular databased methods under the same dataset.

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.