A Survey on Deep Learning Methods for Robot Vision <s> Scene Representation and Classification (Scene Exploration) <s> Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks to bootstrap learning. In particular we consider jointly learning the goal-driven reinforcement learning problem with an unsupervised depth prediction task and a self-supervised loop closure classification task. Using this approach we can learn to navigate from raw sensory input in complicated 3D mazes, approaching human-level performance even under conditions where the goal location changes frequently. We provide detailed analysis of the agent behaviour, its ability to localise, and its network activity dynamics, that show that the agent implicitly learns key navigation abilities, with only sparse rewards and without direct supervision. <s> BIB001 </s> A Survey on Deep Learning Methods for Robot Vision <s> Scene Representation and Classification (Scene Exploration) <s> Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. ::: We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment. ::: The supplementary video can be accessed at the following link: this https URL <s> BIB002
Zhu et al., BIB002 Robot navigation by learning a scene-dependent Siamese network, which receives images from two places as input, and generates motion commands for travelling between them. Scene Representation and Classification (Visual Navigation) Mirowski et al., BIB001 Robot navigation in complex maze-like environments from raw monocular images and inertial information. Use of deep reinforcement learning on a network composed of a CNN followed by two LSTM layers. Multi-task loss considering reward prediction and depth prediction improves learning. Scene Representation and Classification (Visual Navigation)