Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
This addresses the challenge of translating human planning conversations into machine instructions for robots in time-critical domains like military operations and disaster response, representing a novel integration of methods.
The paper tackles the problem of reducing the burden of programming autonomous systems by inferring robot task plans from human team meetings, achieving 83% accuracy in plan inference through a hybrid generative modeling approach with logic-based priors.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.