Human-Robot Interaction, Imitation Learning, Human Motion Prediction, Social Robotics, Robot Vision, SLAM, Navigation
- TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
- TU Darmstadt, Chair of Marketing and HR Management, Department of Law and Economics
Vignesh Prasad joined TU Darmstadt in July 2019 as a Ph.D. student working on Learning Physically Interactive Human-Robot Interaction for Humanoid Social Robots. Vignesh is jointly supervised by Jan Peters, Georgia Chalvatzaki, Dorothea Koert and Ruth Stock-Homburg. His current areas of research include Human-Robot Interaction, Learning from Demonstrations, Human Motion Prediction, and Social Robotics.
Prior to this, Vignesh worked as a researcher in the Machine Vision Group at TCS Innovation Labs, Kolkata under Dr. Brojeshwar Bhowmick, where he worked on Deep Learning for Monocular 3D Reconstruction and Computer Vision. During this time, Vignesh's work won the Best Paper Award at the 2018 Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP). Vignesh pursued his Bachelors and Masters in Computer Science and Engineering from IIIT Hyderabad, India. His Master's thesis titled "Learning Effective Navigational Strategies for Active Monocular Simultaneous Localization and Mapping" was done at the Robotics Research Center under Dr. K. Madhava Krishna in collaboration with Prof. Balaraman Ravindran.
- , 32th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).
- , International Journal of Social Robotics (IJSR), 14, 1, pp.277-293.
- , IEEE-RAS International Conference on Humanoid Robots (Humanoids).
- , Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
- , Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI), Late Breaking Report.
- , International Conference on Social Robotics, Springer.
For a full list of his publications, please see his Google Scholar page.
Supervised Theses and Projects
|Ongoing||Bachelor Thesis||Fabian Hahne||Hierarchical Hidden Markov Models for Interaction Segmentation and Learning|
|Ongoing||Bachelor Thesis||Arne Backstein||Learning Human-Robot Interaction using Normalizing Flows|
|2023||Integrated Project||Frederic Metzler, Martina Gassen, Erik Prescher||I^3: Interactive Iterative Improvement for Few-Shot Action Segmentation||Lisa Scherf, Felix Kaiser|
|2023||Integrated Project||Antonio De Almeida Correia, Yasemin Göksu||Learning Action Representations For Primitives-Based Motion Generation||Alap Kshirsagar|
|2023||Master Thesis||Ruiyong Pi||Bluetooth Low Enery Localization for the Social Robot Zenbo||Sven Schultze|
|2023||Master Thesis||Rukang Xu||SLAM-itation: SLAM-based robotic teleoperation||Suman Pal|
|2023||Bachelor Thesis||Hongzhe Gao||Understanding Haptic Emotions for Human-Robot Handshaking|
|2022||Bachelor Thesis||Erik Prescher||Visual Hierarchical Interaction Recognition and Segmentation|
|2022||Bachelor Thesis||Louis Sterker||Social Affordance Segmentation and Learning using Hidden semi-Markov Models|
|2022||Master Thesis||Oriol Hinojo Comellas||Binaural Sound Localisation with Spiking Neural Networks||Sven Schultze|
|2022||Master Thesis||Yannik Frisch||Analysis of Self-supervised Keypoint Detection Methods for Robot Learning||Ali Younes, Georgia Chalvatzaki|
|2022||Master Thesis||Zhicheng Yang||Exploring Gripping Behaviours and Haptic Emotions for Human-Robot Handshaking|
|2021||Bachelor Thesis||Martina Gassen||Learning a library of Physical Interactions for Social Robots||Dorothea Koert|
|2021||Master Thesis||Maxim Redkin||Personalizing Customer Interactions with Service Robots using Hand Gestures|
|2021||Bachelor Thesis||Lennard Scherbring||Analyzing the role of Physical Interactions on Robot Acceptance|
|2021||Master Thesis||Michel Kohl||Learning Latent Interaction Models using Interaction Primitives|
|2020||Bachelor Thesis||Yug Ajmera||Learning Movement Primitives for Handshaking Behaviours|
|2020||Bachelor Thesis||Mark Baierl||Learning Action Representations For Primitives-Based Motion Generation|