Contextual Recognition Network: Combining DDPG and Contextual Affordances for Robotic Safe Grasping

With the advancement of deep learning, numerous research initiatives have emerged focusing on enabling robots to identify and retrieve target objects within complex domestic environments. However, current research lacks effective integration of contextual affordances information in robotic systems. This paper introduces an intelligent grasping system to facilitate object prediction and safe policy learning for home-use robots. Particularly, we introduce the Context Recognition Network (CRN) to predict the potential failure likelihood of each action. We develop a grasping system based on DDPG (Deep Deterministic Policy Gradient) as the benchmark. We compare the benchmark's performance with that of the CRN-equipped grasping system. Our results indicate that the CRN-equipped grasping system outperforms DDPG by blocking failure action and instead choosing an appropriate pose based on the object prediction to retrieve the object with fewer computational resources.