Faster Motion Planning Using Learned Local Viability Models

Current motion planners, in general, can neither "see" the world around them, nor learn from experience. That is, their reliance on collision tests as the only means of sensing the environment yields a tactile, myopic perception of the world. Such short-sightedness greatly limits any potential for detection, learning, or reasoning about frequently encountered situations. As a result, it is common for current planners to solve and re-solve the same general scenarios over and over, each time none the wiser. We thus propose a general approach for motion planning, as well as a specific illustrative algorithm, in which local sensory information, in conjunction with prior accumulated experience, are exploited to improve planner performance. Our approach relies on learning viability models for the agent's "perceptual space", and the use thereof to direct planning effort. Experiments with three test agents show significant speedups and skill-transfer between environments.