Co-Chair in IEEE WIE-RAS

Georgia will be serving as a co-chair of the IEEE RAS Women in Engineering group, along with Chair Karinne Ramirez Amaro and co-Chair Daniel Leidner.

The Women in Engineering – RAS (WIE-RAS) group was formed by the Member Activities Board (MAB) to inspire, engage and advance women in Robotics and Automation. The WIE committee organizes a series of periodic events and activities to promote the visibility of women leaders and to inspire young girls who want to get involved in engineering.

See more details:

Master Thesis: Discovering neural parts in objects with invertible NNs for robot grasping

In this thesis, we will investigate the use of 3D primitive representations in objects using Invertible Neural Networks (INNs). Through INNs we can learn the implicit surface function of the objects and their mesh. Apart from  extracting the object’s shape, we can parse the object into semantically interpretable parts. In our work our main focus will be to segment the parts in objects that are semantically related to object affordances. Moreover, the implicit representation of the primitive can allow us to compute directly the grasp configuration of the object, allowing grasp planning.

Interested students are expected to have experience with Computer Vision and Deep Learning, but also know how to program in Python using DL libraries like PyTorch. The thesis will be co-supervised by Despoina Paschalidou (Ph.D. candidate at the Max Planck Institute for Intelligent Systems and the Max Planck ETH Center for Learning Systems). Highly motivated students can apply by sending an e-mail expressing your interest to, attaching your CV and transcripts.


[1] Paschalidou, Despoina, Angelos Katharopoulos, Andreas Geiger, and Sanja Fidler. “Neural Parts: Learning expressive 3D shape abstractions with invertible neural networks.” arXiv preprint arXiv:2103.10429 (2021).

[2] Karunratanakul, Korrawe, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, and Siyu Tang. “Grasping Field: Learning Implicit Representations for Human Grasps.” arXiv preprint arXiv:2008.04451 (2020).

[3] Chao, Yu-Wei, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang et al. “DexYCB: A Benchmark for Capturing Hand Grasping of Objects.” arXiv preprint arXiv:2104.04631 (2021).

[4] Do, Thanh-Toan, Anh Nguyen, and Ian Reid. “Affordancenet: An end-to-end deep learning approach for object affordance detection.” In 2018 IEEE international conference on robotics and automation (ICRA), pp. 5882-5889. IEEE, 2018.

Talk @ UofT on accelerating Robot Skill Learning

My talk on “Accelerating Robot Skill Learning with Demonstrations and Models”, that I gave a few days ago at the AI in Robotics Seminar at the University of Toronto is now available online.

In this talk I go through our recent works “Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills” and “Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning” that will appear at ICRA2021.

hessian.AI Connectom fund

iROSA research group has received the hessian.AI Connectom fund, which promotes interdisciplinary research in the hessian.AI ecosystem.

This grant will allow researches from iROSA to work along researches from the Ubiquitous Knowledge Processing lab, led Prof. Dr. Gurevych.

Our project will investigate synergies between Robot Learning and Natural Language Processing towards Learning Long-Horizon Tasks by bridging Object-centric Representations to Knowledge Graphs.

AI Newcomer 2021

AI Newcomer 2021 of the category technical and engineering science is Dr. Georgia Chalvatzaki!

“AI advances robotic research for developing intelligent agents that interact safely and assist humans” – Georgia Chalvatzaki

Article in German by TU Darmstadt press news:

KICamp21 @GeorgiaChal

Master Thesis/Job@DLR: Cross-platform Benchmark of Robot Grasp Planning

Grasp planning is one of the most challenging tasks in robot manipulation. Apart from perception ambiguity, the grasp robustness and the successful execution rely heavily on the dynamics of the robotic hands. The student is expected to research and develop benchmarking environments and evaluation metrics for grasp planning. The development in simulation environments as ISAAC Sim and Gazebo will allow us to integrate and evaluate different robotic hands for grasping a variety of everyday objects. We will evaluate grasp performance using different metrics (e.g., object-category-wise, affordance-wise, etc.), and finally, test the sim2real gap when transferring such approaches from popular simulators to real robots. 

TIAGo++ and Rollin Justin

The student will have the chance to work with different robotic hands (Justin hand, PAL TIAGo hands, Robotiq gripper, Panda gripper, etc.) and is expected to transfer the results to at least two robots (Rollin’ Justin at DLR and TIAGo++ at TU Darmstadt). The results of this thesis are intended to be made public (both the data and the benchmarking framework) for the benefit of the robotics community. 

As this thesis is offered in collaboration with the DLR institute of Robotics and Mechatronics in Oberpfaffenhofen near Munich, the student is expected to work in DLR for a period of 8-months for the thesis. On-site work at the premises of DLR can be expected but not guaranteed due to COVID-19 restrictions. A large part of the project can be carried out remotely. 

Highly motivated students can apply by sending  an e-mail expressing their interest to and

Please attach your CV and your transcripts.


[1] Collins, Jack, Shelvin Chand, Anthony Vanderkop, and David Howard. “A Review of Physics Simulators for Robotic Applications.” IEEE Access (2021).

[2] Bekiroglu, Y., Marturi, N., Roa, M. A., Adjigble, K. J. M., Pardi, T., Grimm, C., … & Stolkin, R. (2019). Benchmarking protocol for grasp planning algorithms. IEEE Robotics and Automation Letters, 5(2), 315-322.

3 papers accepted to ICRA 2021!

Three papers got accepted in ICRA2021, whose topics will be directly extended in the context of the iROSA project.

Accepted papers:

Tosatto, S.; Chalvatzaki, G.; Peters, J. (2021). Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   See Details BibTeX Reference

Li, Q.; Chalvatzaki, G.; Peters, J.; Wang, Y. (2021). Directed Acyclic Graph Neural Network for Human Motion Prediction, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   See Details BibTeX Reference

Morgan, A.; Nandha, D.; Chalvatzaki, G.; D’Eramo, C.; Dollar, A.; Peters, J. (2021). Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   See Details BibTeX Reference