Surface exploration of planetary environments with current robotic technologies relies heavily on human control and power-hungry active sensors to perform even the most elementary low-level functions. Ideally, a robot should be capable of autonomously exploring and interacting within an unknown environment without relying on human input or suboptimal sensors. Behaviors such as exploration of unknown environments, memorizing locations of obstacles or objects, building and updating a representation of the environment, and returning to a safe location, are all tasks that constitute typical activities efficiently performed by animals on a daily basis. Phase I of this proposal will focus on design of an adaptive robotic multi-component neural system that captures the behavior of several brain areas responsible for perceptual, cognitive, emotional, and motor behaviors. This system makes use of passive, potentially unreliable sensors (analogous to animal visual and vestibular systems) to learn while navigating unknown environments as well as build usable and correctable representations of these environments without requiring a Global Navigation Satellite System (GNSS). In Phase I, Neurala and the Boston University Neuromorphics Lab, will construct a virtual robot, or animat, to be developed and tested in an extraterrestrial virtual environment. The animat will use passive sensors to perform a spatial exploration task. The animat will start exploring from a recharging base, autonomously plan where to go based on past exploration and its current motivation, develop and correct an internal map of the environment with the locations of obstacles, select the shortest path of return to its recharging base before battery depletion, then extract the resulting explored map into a human-readable format. In Phase II Neurala will enhance and translate the model to low-power neuromorphic hardware and collaborate with iRobot to test the model in a robotics platform.