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Active articulation model estimation through interactive perception
2015
2015 IEEE International Conference on Robotics and Automation (ICRA)
We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty about the articulation model. The full system is experimentally evaluated using a PR2
doi:10.1109/icra.2015.7139655
dblp:conf/icra/HausmanNOS15
fatcat:7rfvc3dfobgdxi4kxqmj7gsiim