Unsupervised robot learning to predict person motion
2015 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2015, no June, 691-696 p.Conference paper (Refereed)
Socially interacting robots will need to understand the intentions and recognize the behaviors of people they come in contact with. In this paper we look at how a robot can learn to recognize and predict people's intended path based on its own observations of people over time. Our approach uses people tracking on the robot from either RGBD cameras or LIDAR. The tracks are separated into homogeneous motion classes using a pre-trained SVM. Then the individual classes are clustered and prototypes are extracted from each cluster. These are then used to predict a person's future motion based on matching to a partial prototype and using the rest of the prototype as the predicted motion. Results from experiments in a kitchen environment in our lab demonstrate the capabilities of the proposed method.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. no June, 691-696 p.
Forecasting, Robots, Target tracking, Path-based, People tracking, Rgb-d cameras, Robotics
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-176132DOI: 10.1109/ICRA.2015.7139254ISI: 000370974900100ScopusID: 2-s2.0-84938228810OAI: oai:DiVA.org:kth-176132DiVA: diva2:875654
2015 IEEE International Conference on Robotics and Automation, ICRA 2015, 26 May 2015 through 30 May 2015
FunderEU, FP7, Seventh Framework Programme, 600623
QC 20151201. QC 201604112015-12-012015-11-022016-04-11Bibliographically approved