Algorithms for robust autonomous navigation in human environments

Abstract

Today's robots are designed for humans, but are rarely deployed among humans. This thesis addresses problems of perception, planning, and safety that arise when deploying a mobile robot in human environments. A first key challenge is that of quickly navigating to a human-specified goal - one with known semantic type, but unknown coordinate - in a previously unseen world. This thesis formulates the contextual scene understanding problem as an image translation problem, by learning to estimate the planning cost-to-go from aerial images of similar environments. The proposed perception algorithm is united with a motion planner to reduce the amount of exploration time before finding the goal. In dynamic human environments, pedestrians also present several important technical challenges for the motion planning system. This thesis contributes a deep reinforcement learning-based (RL) formulation of the multiagent collision avoidance problem, with relaxed assumptions on the behavior model and number of agents in the environment. Benefits include strong performance among many nearby agents and the ability to accomplish long-term autonomy in pedestrian-rich environments. These and many other state-of-the-art robotics systems rely on Deep Neural Networks for perception and planning. However, blindly applying deep learning in safety-critical domains, such as those involving humans, remains dangerous without formal guarantees on robustness. For example, small perturbations to sensor inputs are often enough to change network-based decisions. This thesis contributes an RL framework that is certified robust to uncertainties in the observation space.