Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral manner, making no special attempt to shield users from relatively rare but serious outcomes, even if such shielding might cause little loss of performance. Furthermore, such algorithms typically make no provisions to ensure safety in the presence of inaccuracies in the models on which they were trained, beyond adding a cost-of-collision and some domain randomization while training, in spite of the formidable complexity of the environments in which they operate. In this paper, we present a novel distributional RL algorithm that not only learns an uncertainty-aware policy, but can also change its risk measure without expensive fine-tuning or retraining. Our method shows superior performance and safety over baselines in partially- observed navigation tasks. We also demonstrate that agents trained using our method can adapt their policies to a wide range of risk measures at run-time.

Deep reinforcement learning (RL) is being actively studied for robot navigation due to its promise of superior performance and robustness. However, most existing deep RL navigation agents are trained using fixed parameters, such as maximum velocities and weightings of reward components. Since the optimal choice of parameters depends on the use-case, it can be difficult to deploy such existing methods in a variety of real-world service scenarios. In this paper, we propose a novel deep RL navigation method that can adapt its policy to a wide range of parameters and reward functions without expensive retraining. Additionally, we explore a Bayesian deep learning method to optimize these parameters that requires only a small amount of preference data. We empirically show that our method can learn diverse navigation skills and quickly adapt its policy to a given performance metric or to human preference. We also demonstrate our method in real-world scenarios.

Mobile robots are required to navigate freely in a complex and crowded environment in order to provide services to humans. For this navigation ability, deep reinforcement learning (DRL)-based methods are gaining increasing attentions. However, existing DRL methods require a wide field of view (FOV), which imposes the usage of high-cost lidar devices. In this paper, we explore the possibility of replacing expensive lidar devices with affordable depth cameras which have a limited FOV. First, we analyze the effect of a limited field of view in the DRL agents. Second, we propose a LSTM agent with Local-Map Critic (LSTM-LMC), which is a novel DRL method to learn efficient navigation in a complex environment with a limited FOV. Lastly, we introduce the dynamics randomization technique to improve the robustness of the DRL agents in the real world. We found that our method with a limited FOV can outperform the methods having a wide FOV but limited memory. We provide the empirical evidence that our method learns to implicitly model the surrounding environment and dynamics of other agents. We also show that a robot with a single depth camera can navigate through a complex real-world environment using our method.