Detail of the student project

List
Topic:Rozpoznávání předmětů pomocí vidění a dotyku robotickými rukami
Department:Katedra kybernetiky
Supervisor:Mgr. Matěj Hoffmann, Ph.D.
Announce as:Diplomová práce, Bakalářská práce, Semestrální projekt
Description:The goal of this project is to use different robotic arms and grippers (KUKA LBR iiwa with Barrett Hand, UR10 with OnRobot RG6 or QB Soft Hand, Kinova Gen3 with Robotiq 2F-85) to aid visual perception, focusing in particular on object material properties (stiffness, surface roughness, etc.). For visual perception, state-of-the-art algorithms from our European partners (https://sites.google.com/view/ipalm/) will be employed to obtain estimates of object pose, shape, and material. Based on these priors, the goal is to develop an object exploration strategy to verify these hypotheses. The actions may involve: (1) manipulation (e.g., squeezing, pushing) and (2) visual exploration using a moving RGB-D camera (Intel Realsense D410 in the wrist of Kinova Gen3). The grippers / handshave different feedback signals available - the Barrett Hand, for example, has 96 tactile sensors and 3 fingertip torque sensors.
Video illustration: Barrett Hand grasping a soft object: https://youtu.be/J6YXZgbDjBw
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.

Davis, A., Bouman, K. L., Chen, J. G., Rubinstein, M., Durand, F., & Freeman, W. T. (2015). Visual vibrometry: Estimating material properties from small motion in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5335-5343).

Nikandrova, E., Laaksonen, J., & Kyrki, V. (2014). Towards informative sensor-based grasp planning. Robotics and Autonomous Systems, 62(3), 340-354.

Pumarola, A., Agudo, A., Porzi, L., Sanfeliu, A., Lepetit, V., & Moreno-Noguer, F. (2018). Geometry-aware network for non-rigid shape prediction from a single view. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4681-4690).
Responsible person: Petr Pošík