Graduation Colloquium Artificial Intelligence Yikun Li

Title: Learning to Detect Grasp Affordances of 3D Objects using Deep Convolutional Neural Networks

Abstract:

Grasp affordances detection is one of the challenging tasks in robotics because it must predict the grasp configuration for the object of interest in real-time to enable the robot to interact with the environment. In this thesis, we present a new deep learning approach to detect object affordances for a given 3D object.  The method trains a Convolutional Neural Network (CNN) to learn a set of grasping features from RGB-D images. We named our approach Res-U-Net since the architecture of the network is designed based on U-Net structure and some residual network-styled blocks. It devised to be robust and efficient to compute and use. A set of preliminary experiments has been performed to assess the performance of the proposed approach regarding grasp success rate on simulated robotic scenarios. Experiments validate the promising performance of the proposed architecture on a subset of ShapeNet dataset and simulated robot scenarios.