Explore, Approach, and Terminate: Evaluating Subtasks in Active Visual Object Search Based on Deep Reinforcement Learning Searching for objects and distinguishing taskrelevant objects from others is a key requirement for service robots. We propose a reinforcement learning solution to the active visual object search problem. Our method successfully learns to explore the environment, to approach the target object, and to decide when to terminate the search as the target object has been found. We demonstrate the efficiency of our solution on a dataset of realworld images collected by a robot. Our approach outperforms statespace planning or other baseline search strategies, reaching a higher success rate in a shorter time. We also study individual subtasks of active visual object search. Although strong baselines exist for the subtasks, our RL solution outperforms them in the overall search task.

Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception? The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for realworld robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for realworld noise (motion blur, occlusion, etc.). We evaluated several stateoftheart object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of realworld robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.