Title | Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior |
Publication Type | Conference Proceedings |
Year of Conference | 2013 |
Authors | Kim, B., C. M. Chacha, and J. A. Shah |
Conference Name | AAAI Conference on Artificial Intelligence (AAAI) [29% Acceptance Rate] |
Date Published | 07/2013 |
Abstract | 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. |
URL | https://interactive.mit.edu/sites/default/files/documents/aaaiKimShah13_cameraReady.pdf |