Investigating the effect of grasp stiffness on grasp success prediction
Assessing whether an acquired grasp will successfully withstand the expected disturbance load is a necessary step in autonomous robotic grasp planning and execution pipelines. To this end, features obtained from tactile sensors on the hand's fingerpads have shown to be highly valuable [1, 2].
The aim of this thesis is to extend the recent grasp success prediction framework in  with an explicit notion of the grasp stiffness. To this end, the student will set up an experimental environment using ABB's YuMi robot and perform a series of grasping experiments to investigate the effects of grasp stiffness. An optional extension of the thesis is to augment the analytic framework with a model learned from data collected during the grasping trials. Overall, this thesis will have a strong scientific and experimental character.
- Augment an existing grasp success prediction metric with a notion of stiffness
- Implement a grasp stiffness controller on the ABB YuMi platform available in the lab
- Perform a series of grasp experiments to evaluate the new metric
- Use machine learning to incorporate the collected experimental data into the grasp success prediction approach
- Solid C++/Matlab knowledge
- Experience in the ROS ecosystem would be helpful
Contact: Robert Krug
 Krug, Robert, et al. "Analytic grasp success prediction with tactile feedback." 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016.
 Bekiroglu, Yasemin, et al. "Assessing grasp stability based on learning and haptic data." IEEE Transactions on Robotics 27.3 (2011): 616-629.