Publications and communications of Renaud Detry

Kopicki, M., Detry, R., Adjigble, M., Stolkin, R., Leonardis, A., & Wyatt, J. (2015). One shot learning and generation of dexterous grasps for novel objects. International Journal of Robotics Research. doi:10.1177/0278364915594244

Krishna Moorthy Parvathi, S. M., Detry, R., Boigelot, B., & Mercatoris, B. (05 March 2014). A vision-based autonomous inter-row weeder [Paper presentation]. ENVITAM PhD Student Day, Université catholique de Louvain, Louvain-la-Neuve, Belgium.

Bodenhagen, L., Detry, R., Piater, J., & Krüger, N. (2011). What a successful grasp tells about the success chances of grasps in its vicinity. In ICDL-EpiRob.

Detry, R., Kraft, D., Kroemer, O., Bodenhagen, L., Peters, J., Krüger, N., & Piater, J. (2011). Learning Grasp Affordance Densities. Paladyn. Journal of Behavioral Robotics, 2 (1), 1-17. doi:10.2478/s13230-011-0012-x

Detry, R., & Piater, J. (2011). Grasp Generalization Via Predictive Parts [Paper presentation]. Austrian Robotics Workshop.

Detry, R., & Piater, J. (2010). Continuous Surface-point Distributions for 3D Object Pose Estimation and Recognition. In Asian Conference on Computer Vision.

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., & Peters, J. (2010). Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1586-1591). doi:10.1109/IROS.2010.5650088

Kraft, D., Detry, R., Pugeault, N., Başeski, E., Guerin, F., Piater, J., & Krüger, N. (2010). Development of Object and Grasping Knowledge by Robot Exploration. IEEE Transactions on Autonomous Mental Development, 2 (4), 368-383. doi:10.1109/TAMD.2010.2069098

Kroemer, O., Detry, R., Piater, J., & Peters, J. (2010). Combining Active Learning and Reactive Control for Robot Grasping. Robotics and Autonomous Systems. doi:10.1016/j.robot.2010.06.001

Detry, R., Pugeault, N., & Piater, J. (2009). A Probabilistic Framework for 3D Visual Object Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2009.64