Elmar Rückert
Research output
- Published
Model-free Probabilistic Movement Primitives for physical interaction
Paraschos, A., Rueckert, E., Peters, J. & Neumann, G., 11 Dec 2015, IEEE International Conference on Intelligent Robots and Systems. p. 2860-2866 7 p. (IEEE International Conference on Intelligent Robots and Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models
Nwankwo, L. & Rückert, E., 11 Mar 2024.Research output: Contribution to conference › Poster › Research › peer-review
- Published
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training
Dave, V., Lygerakis, F. & Rückert, E., 28 Jan 2024, In: Proceedings / IEEE International Conference on Robotics and Automation.Research output: Contribution to journal › Conference article › peer-review
- Published
Physics-informed neural network for predicting Gibbs free energy
Vincely, C., Sakic, A., Dave, V., Povoden-Karadeniz, E., Rückert, E. & Holec, D., 2023.Research output: Contribution to conference › Poster › Research
- Published
Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks
Krukenfellner, P., Rückert, E. & Flachberger, H., 4 Oct 2024, In: IEEE sensors journal. 24.2024, 22, p. 38232-38243 12 p.Research output: Contribution to journal › Article › Research › peer-review
- Published
Predicting full-arm grasping motions from anticipated tactile responses
Dave, V. & Rueckert, E., 26 Sept 2022, IEEE-RAS International Conference on Humanoid Robots. (IEEE-RAS International Conference on Humanoid Robots).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- E-pub ahead of print
Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined with a Flow Controller
Jamsek, M., Kunavar, T., Bobek, U., Rueckert, E. & Babic, J., 25 Mar 2021, (E-pub ahead of print) In: IEEE robotics and automation letters. 6.2021, 3, p. 4417-4424 8 p., 9387088.Research output: Contribution to journal › Article › Research › peer-review
Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control
Rückert, E., Čamernik, J., Peters, J. & Babič, J., 16 Apr 2020, In: Scientific reports. 6.2016, 1, 12 p., 28455 / 6694.Research output: Contribution to journal › Article › Research › peer-review
Probabilistic movement primitives under unknown system dynamics
Paraschos, A., Rueckert, E., Peters, J. & Neumann, G., 25 Apr 2018, (E-pub ahead of print) In: Advanced robotics. 32.2018, 6, p. 297-310 14 p.Research output: Contribution to journal › Article › Research › peer-review
- Published
REAL-2019: Robot open-Ended Autonomous Learning competition
Cartoni, E., Mannella, F., Santucci, V. G., Triesch, J., Rückert, E. & Baldassarre, G., 2019, Proceedings of Machine Learning Research: 3rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019. Vol. 123.2019. p. 142-152 11 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
