Behavior Cloning-Based Robot Active Object Detection With Automatically Generated Data and Revision Method

Abstract

Active object detection (AOD), one of the greatest challenges in the robotics field, is the main focus of this article. Most current AOD methods are developed by reinforcement learning (RL) algorithms while they can be further improved in the aspects of training time, training efficiency, model performance, and model prediction. Therefore, different from the existing works, we propose an AOD method based on behavior cloning trained by automatically generated data. We transform the AOD task into an action classification problem to not only shorten the training time but also improve the training efficiency and model performance. As there is no available expert data for training the presented classification-based AOD model, we design an autonomous method of data generation to avoid the large amounts of manual annotations. We introduce a multiinput network for better obstacle avoidance and AOD performance, where the depth image is added to help the robot to perceive distance information of environments and objects. Moreover, we develop a revision method for model prediction to reduce the accumulation of compounding error, which improves the successful rate of the long path AOD tasks effectively. We extensively evaluate our method on an AOD dataset by the comparable experiments and the ablation study, proving that our approach outperforms other methods in AOD performance and efficiency. In addition, the AOD experiments in the real-world scenario with a TIAGo robot indicate the validity of our method.