Local driving assistance from demonstration for mobility aids

Active assistive mobility systems are largely limited to a-priori mapped environments, whereas their reactive assistive counterparts are in general location independent and focus on the provision of collision avoidance in the immediate space surrounding the platform. This paper presents a framework capable of providing active short-term navigation, combining the intelligence of active assistance with the freedom of location independence. Demonstration data from an able expert while driving the mobility aid in a standard indoor setting is used off-line to learn reference behavioral models of navigation given perceptual information from the platform surroundings and the input controls exerted by the user while navigating. These serve as the foundation for on-line probabilistic short-term destination inference using the instantaneously available data from the user and on-board sensors. This is coupled with a real-time stochastic optimal path generation able to exploit the same short term demonstration paths from the expert with the belief they capture both the driver's awareness of the platform's physical geometry and appropriate behaviors for their surroundings. Experimental results with users of varying proficiency in a setting unvisited in training data show promise in using the framework in assisting users experiencing difficulty in safe power mobility aid use.