Research on Perception and Control Technology for Dexterous Robot Operation

Robotic grasping in cluttered environments is a fundamental and challenging task in robotics research. The ability to autonomously grasp objects in cluttered scenes is crucial for robots to perform complex tasks in real-world scenarios. Conventional grasping is based on the known object model in a structured environment, but the adaptability of unknown objects and complicated situations is constrained. In this paper, we present a robotic grasp architecture of attention-based deep reinforcement learning. To prevent the loss of local information, the prominent characteristics of input images are automatically extracted using a full convolutional network. In contrast to previous model-based and data-driven methods, the reward is remodeled in an effort to address the sparse rewards. The experimental results show that our method can double the learning speed in grasping a series of randomly placed objects. In real-word experiments, the grasping success rate of the robot platform reaches 90.4%, which outperforms several baselines.

Automated summary

This paper presents a robotic grasp architecture based on attention-based deep reinforcement learning. Prominent characteristics of input images are automatically extracted using a full convolutional network to prevent the loss of local information. Unlike previous model-based and data-driven methods, the reward is remodeled to address sparse rewards. Experimental results show that our method can double the learning speed in grasping randomly placed objects. In real-world experiments, the grasping success rate of the robot platform reaches 90.4%, outperforming several baselines.