Inferring robot task plans from human team meetings: A generative modeling approach with logic-based prior
Author(s)Kim, Been; Chacha, Caleb M.; Shah, Julie A
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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. © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal of Artificial Intelligence Research
Association for Computing Machinery
Kim, Been, Caleb M. Chacha and Julie A. Shah. "Inferring team task plans from human meetings: a generative modeling approach with logic-based prior." Journal of Artificial Intelligence Research 52, no 1 (January 2015): 361-398.