Dr. Kaboli is a postdoctoral research associate at the Institute for Advance Study (IAS) and the Institute for Cognitive Systems (ICS). He is a part of the research project from TUM-IAS Hans Fischer Senior fellow.
He was awarded a Ph.D. degree in robotics, artificial intelligence, and tactile sensing with the highest distinction (summa cum laude) from the Technical University of Munich (TUM) in 2017.
He received his Master's degree in signal processing and machine learning under the supervision of Prof. Danica Kragic from the Royal Institute of Technology (KTH), Sweden in 2011.
In April 2013 he was awarded a three-year Marie Curie scholarship in order to pursue his Ph.D. at the Institute for Cognitive Systems (ICS).
In March 2011, he received an internship scholarship from the Swiss National Foundation for 18 months in order to continue his research as a research assistant at the Idiap Research Institute and Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland.
From September 2015 till January 2016, he spent 5 months as a visiting research scholar at the Intelligent Systems and Informatics lab (ISI) directed by Prof. Yasuo Kuniyoshi at the University of Tokyo, Japan.
He has also been a visiting researcher at the Human Robotics lab, the department of Bioengineering at the Imperial College London supervised by Prof. Etienne Burdet from February till April 2014. In November 2013, he visited the Shadow Robot Company for two months.
• Marie Curie scholarship 2013-2016
• Travel Grant, TUM Graduate School 2016
• Swiss National Foundation 2011-2012
Humans learn much of our tactile perception by interacting with the environment. Observing tactual exploration of human, we carefully interact with the objects in the world, repeatedly trying to pick up an object, learning to perceive the world through our skin. Most of the research in tactile sensing has focused on building and characterizing tactile sensors. Very little work has been done on robot learning from tactile sensors. Such issues are important for robots to be able to learn autonomously and to interact safe with the environment i.e. without causing any harm to it or to the objects with which it is interacting.
Dr. Kaboli research focus is tactile perception and learning in robotics systems via artificial robotic skin.
During his research work, he developed the first full-fledged probabilistic frameworks for active tactile perception and learning as well as active tactile transfer learning for autonomous robotic systems. For instance, he developed a complete tactile-based probabilistic framework for the robotic systems to autonomously explore environment to localize objects, estimate their orientation, and compute their geometrical information. Then the robot autonomously learns about objects via their multiple tactual prosperities. For the first time in field of robotic and AI, Dr. Kaboli designed and developed novel methods to enable the autonomous robot to actively re-uses their prior tactile knowledge or past tactile experience while learning new task with only one trial (Active Tactile Transfer Learning).
- Active tactile exploration and learning
- Active tactile transfer learning
- Tactile-based object modeling and recognition
- Tactile based grasping and in-hand object manipulation
- Tactile feature extraction and feature learning
- Sensor Fusion
- Tactile SLAM
- Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects. Sensors and Actuators A: Physical, 18 (2), 2018 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
- Tactile-based Active Object Discrimination and Target Object Search in an Unknown Workspace. Autonomous Robots, 2018, 1573-7527 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
- Tactile-based Object Center of Mass Exploration and Discrimination. IEEE International Conference on Humanoid Robots (Humanoids), 2017 more… BibTeX Full text (mediaTUM)
- Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment using Multimodal Robotic Skin. International Journal of Humanoid Robotics (IJHR), 2017 more… BibTeX Full text (mediaTUM)
- A Tactile-based Framework for Active Object Learning and Discrimination using Multi-modal Robotic Skin. IEEE Robotics and Automation Letters 2 (4), 2017, 2143-2150 more… BibTeX Full text (mediaTUM)
- Tactile-based Manipulation of Deformable Objects with Dynamic Center of Mass. IEEE-RAS International Conference on Humanoid Robots 2016, 2016 more… BibTeX Full text (mediaTUM)
- Re-using Prior Tactile Experience by Robotic Hands to Discriminate In-Hand Objects via Texture Properties. IEEE International Conference on Robotics and Automation (ICRA 2016), 2016 more… BibTeX Full text (mediaTUM)
- Dexterous Hands Learn To Re-Use The Past Experience To Discriminate In-Hand Objects From The Surface Texture. 33rd Annual Conference of the Robotics Society of Japan (RSJ 2015), 2015 more… BibTeX Full text (mediaTUM)
- Humanoids Learn Touch Modalities Identification via Multi-Modal Robotic Skin and Robust Tactile Descriptors. Advanced Robotics , 2015 more… BibTeX Full text (mediaTUM)
- In-Hand Object Recognition via Texture Properties with Robotic Hands, Artificial Skin, and Novel Tactile Descriptors. IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 2015 more… BibTeX Full text (mediaTUM)
- New Materials and Advances in Making Electronic Skin for Interactive Robots. Advanced Robotics , 2015 more… BibTeX Full text (mediaTUM)