DataDriven Video Grasping Classification for LowPower Embedded System Videobased hand grasp analysis can support both robotics and prosthetics. Indeed, computational aspects represent a major issue, as hand grasp analysis is expected to support grasping systems that are hosted on lowpower embedded systems. This paper proposes a framework for videobased grasping classification that is designed for implementation on resourceconstrained devices. The framework adopts a fully datadriven strategy and relies on deep learning to deal with advanced analysis of video signals. Nonetheless, the overall design takes advantage of CNN architectures that can cope with the constraints imposed by embedded systems. The experimental session involved a realworld dataset containing daily life activities collected using egocentric perspective. In addition, the complete inference system is implemented on a NVIDIA JetsonTX2 obtaining real time performances. The results confirm that the proposed system can suitably balance the trade off between accuracy and computational costs.

Fast Convolutional Neural Network for RealTime Robotic Grasp Detection The development of the robotics field has not yet allowed robots to execute, with dexterity, simple actions performed by humans. One of them is the grasping of objects by robotic manipulators. Aiming to explore the use of deep learning algorithms, specifically Convolutional Neural Networks (CNN), to approach the robotic grasping problem, this work addresses the visual perception phase involved in the task. To achieve this goal, the dataset xe2x80x9cCornell Graspxe2x80x9d was used to train a CNN capable of predicting the most suitable place to grasp the object. It does this by obtaining a grasping rectangle that symbolizes the position, orientation, and opening of the robotu0027s parallel grippers just before the grippers are closed. The proposed system works in realtime due to the small number of network parameters. This is possible by means of the data augmentation strategy used. The efficiency of the detection is in accordance with the state of the art and the speed of prediction, to the best of our knowledge, is the highest in the literature.