Estimation of inertial parameters of manipulator loads and links
International Journal of Robotics Research
Instance-Based Learning Algorithms
Machine Learning
The calibration index and taxonomy for robot kinematic calibration methods
International Journal of Robotics Research
Artificial Intelligence Review - Special issue on lazy learning
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Robotic Perception of Material: Experiments with Shape-Invariant Acoustic Measures of Material Type
The 4th International Symposium on Experimental Robotics IV
Modeling of Robot Dynamics Based on a Multi-Dimensional RBF-Like Neural Network
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Towards 3D Point cloud based object maps for household environments
Robotics and Autonomous Systems
HERB: a home exploring robotic butler
Autonomous Robots
Interactive learning of the acoustic properties of household objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
International Journal of Robotics Research
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In this paper we propose a method for interactive recognition of household objects using proprioceptive and auditory feedback. In our experiments, the robot observed the changes in its proprioceptive and auditory sensory streams while performing five exploratory behaviors (lift, shake, drop, crush, and push) on 50 common household objects (e.g. bottles, cups, balls, toys, etc.). The robot was tasked with recognizing the objects it was manipulating by feeling them and listening to the sounds that they make without using any visual information. The results show that both proprioception and audio, coupled with exploratory behaviors, can be used successfully for object recognition. Furthermore, the robot was able to integrate feedback from the two modalities, to achieve even better recognition accuracy. Finally, the results show that the robot can boost its recognition rate even further by applying multiple different exploratory behaviors on the object.