Task Modeling in Imitation Learning using Latent Variable Models
2010 (English)In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, 458-553 p.Conference paper (Refereed)
An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations. In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. Our model is capable to infer the task using only partial observations. Further, our framework allows the robot, given partial knowledge of the scene, to reason about what information streams to acquire in order to disambiguate the state-space the most. We present results for task classification within and also reason about different features discriminative power for different classes of tasks.
Place, publisher, year, edition, pages
2010. 458-553 p.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-50658DOI: 10.1109/ICHR.2010.5686348ScopusID: 2-s2.0-79851477094OAI: oai:DiVA.org:kth-50658DiVA: diva2:462371
2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010. Nashville, TN. 6 December 2010 - 8 December 2010
QC 201112082011-12-072011-12-072011-12-08Bibliographically approved