Mind Reading for Social Robots: Stochastic Models of Intent Recognition
AuthorKelley, Richard Charles
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Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. To build robots that reliably function in the human social world, we must develop models that robots can use to mimic the intent recognition skills found in humans. In this work, we propose an approach that allows a physical robot to detect the intentions of others based on experience acquired through its own sensory-motor capabilities, later using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses Hidden Markov Models (HMMs) designed to model a robot's experience and interaction with the world when performing various actions. We augment this baseline intent recognition system with a framework that supports the use of contextual information to improve the overall system's performance. Additionally, we explore the use of an evolutionary algorithm for solving the decoding problem for generative stochastic models, in our intent recognition application and in other domains. We validate all of our approaches on physical robots that classify the intentions of several people performing various activities in a number of scenarios.