This abstract is proposing a challenging problem: to infer a human's mental state - intent and belief - from an observed RGBD video for human-robot interaction. The task is to integrate symbolic reasoning, a field wellstudied within A.I. domains, with the uncertainty native to computer vision strategies. Traditional A.I. strategies for plan inference typically rely on first-order logic and closed world assumptions which struggle to take into account the inherent uncertainty of noisy observations within a scene. Computer vision relies on patternrecognition strategies that have difficulty accounting for higher-level reasoning and abstract representation of world knowledge. By combining these two approaches in a principled way under a probabilistic programming framework, we define new computer vision tasks such as actor intent prediction and belief inference from an observed video sequence. Through inferring a human's theory of mind, a robotic agent can automatically determine a human's goals to collaborate with them.
展开▼
