Iman Nematollahi

Iman Nematollahi

PhD Student in Robot Learning

University of Freiburg


I am a Ph.D. student at the Autonomous Intelligent Systems Lab of the University of Freiburg, supervised by Prof. Dr. Wolfram Burgard. My research focuses on robot manipulation and deep learning to enable robots to develop an intuitive understanding of physics from past unlabeled interaction experiences. This intuitive physics model allows robots to comprehend the 3D dynamics of the surrounding world in order to predict plausible future outcomes and learn a diverse repertoire of adaptive and generalizable skills.


  • Robot Learning
  • Learning Intuitive Physics
  • Self-Supervised Learning
  • PhD in Robot Learning, now

    University of Freiburg

  • MSc in Embedded Systems, 2018

    University of Freiburg

  • BSc in Electrical Engineering, 2015

    Shahid Beheshti University


Research Assistant
Jan 2019 – Present Germany
Student Research Assistant
Aug 2017 – Oct 2017 Germany
Implementation of Dynamic Obstacle Avoidance for KUKA OmniRob
Student Research Assistant
Nov 2016 – Oct 2018 Germany
  • Low power hardware implementation of an Early Seizure Detection Convolutional Neural Network algorithm on FPGA
  • Continuous and simultaneous readout of ADC channels using SPI and USB protocols via FPGA
Electrical Engineer
Mahya Intelligent Transportation Systems
Sep 2012 – Sep 2014 Iran
As a member of Electrical team, I was trained to design, implement and maintain software and hardware requirements of Tehran traffic control devices.
Student Research Assistant
Shahid Beheshti University - Microprocessors Lab
Mar 2014 – Jun 2014 Iran
Design and Implementation of a novel Class Attendance System using AVR Microcontrollers and RFID Modules (used in the 22nd Iranian Conference on Electrical Engineering).


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T3VIP: Transformation-based 3D Video Prediction
IROS 2022 - Kyoto, Japan
T3VIP: Transformation-based 3D Video Prediction
Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models
ICRA 2022 - Philadelphia, USA
Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models
Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection
EMBC 2018 - Honolulu, Hawaii