|Topic:||Haptic exploration and categorization of objects using robotic grippers|
|Supervisor:||Mgr. Matěj Hoffmann Ph.D.|
|Description:||The goal of this project is to use different robotic arms and grippers (KUKA LBR iiwa with Barrett Hand; UR10 with QB Soft Hand or OnRobot RG6) to explore different objects and collect data from proprioceptive, tactile, and force feedback. Different clustering or classification algorithms will be employed on this data to differentiate between the objects, focusing in particular on properties that can only be extracted from haptic exploration (manipulating the objects) such as elasticity, surface properties, etc. In a second step, the choice of grasping actions that aid recognition will be studied. Finally, priors extracted from vision or other sources (Internet – linguistic description) and can be also employed. This work is part of a newly starting European project IPALM (Interactive Perception-Action-Learning for Modelling Objects).
|Instruction:||1.Familiarization with robotic platforms.
2. Pilot data collection - grasping different objects with different grippers.
3. First clustering / categorization of objects from obtained data.
4. Optimizing actions (grasps) to improve categorization.
[Combining with priors from other sources]
|Bibliography:||Bajcsy, R., Aloimonos, Y., & Tsotsos, J. K. (2018). Revisiting active perception. Autonomous Robots, 42(2), 177-196.
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., & Sukhatme, G. S. (2017). Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6), 1273-1291.
Hoffmann, M.; Stepanova, K. & Reinstein, M. (2014), 'The effect of motor action and different sensory modalities on terrain classification in a quadruped robot running with multiple gaits', Robotics and Autonomous Systems 62, 1790-1798.
Nikandrova, E., & Kyrki, V. (2015). Category-based task specific grasping. Robotics and Autonomous Systems, 70, 25-35.