Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

Autonomous navigation is one of the core problems in mobile robotics. It can roughly be characterized as the ability of a robot to get from its current position to a designated goal location solely based on the input it receives from its on-board sensors. A popular approach to this problem relies on the successful combination of a series of different algorithms for the problems of simultaneous localization and mapping (SLAM), localization in a given map as well as path planning and control, all of which often depend on additional information given to the agent. Although individually the problems of SLAM, localization, path planning and control are well understood [1], [2], [3], and a lot of progress has been made on learning control [4], they have mainly been treated as separable problems within robotics and some often require human assistance during setup-time. For example, the majority of SLAM solutions are implemented as passive procedures relying on special exploration strategies or a human controlling the robot for sensory data acquisition. In addition, they typically require an expert to check as to whether the obtained map is accurate enough for path planning and localization. Our goal in this paper is to make first steps towards a solution for navigation tasks without explicit localization, mapping and path planning procedures.