Name
Title | ||
|---|---|---|
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. |
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. |
Year | Venue | Keywords |
|---|---|---|
2013 | national conference on artificial intelligence | machine learning,robotics |
DocType | Volume | Citations |
Conference | abs/1306.0963 | 1 |
PageRank | References | Authors |
0.36 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Been Kim | 1 | 353 | 21.44 |
| Caleb M. Chacha | 2 | 9 | 1.38 |
| Julie A. Shah | 3 | 606 | 57.51 |