A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially acceptable manner. In this talk I will describe new methods based on imitation and reinforcement learning which we have developed to allow robots to achieve socially adaptive path planning in human environments. Performance of these methods will be illustrated using a smart power wheelchair developed in our group, called the SmartWheeler.