Toward Sim-to-Real Directional Semantic Grasping
Toward Sim-to-Real Directional Semantic Grasping
We address the problem of directional semantic grasping, that is, grasping a specific object from a specific direction. We approach the problem using deep reinforcement learning via a double deep Q-network (DDQN) that learns to map downsampled RGB input images from a wrist-mounted camera to Q-values, which are then translated …