We propose an optimization based grasp synthesis framework, GRaCE, to generate context-specific grasps in complex scenarios. We test GRaCE in a simulator and a real-world grasping tasks.
We propose a sampling-based grasp synthesis framework, GraspFlow, to generate context-specific grasps. We test GraspFlow in a real-world table-top grasping task.
We show that robots achieve fast classification of textures through Neural Encoding and Spiking Neural Network.
We show that iCub robot classifies the surface textures with both sliding and touch movements under loose constraints with high accuracy.