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Hamidreza Kasaei


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Subject Open-Ended 3D Object Category Learning and Recognition
Advisor Luís Seabra Lopes, Ana Maria Tomé
Group Intelligent Robotics and Systems
Status PhD student
Starts 2012/10/01
Ends 2017/09/30
Country Iran
Past projects
Personal page

S. Hamidreza Kasaei joined the IEETA and IRIS Labs as a Ph.D. student in October 2012, to work on FP7 Project named "RACE: Robustness by Autonomous Competence Enhancement” under the supervision of Luís Seabra Lopes and Ana Maria Tomé . During his Ph.D., Hamidreza is working on conceptualizing 3D objects, such that robots’ performance on object recognition improves with accumulated experiences and conceptualizations. This project is based on 3D environment perception and open-ended learning, as well as human-robot interaction for labeling objects and providing feedback.

Before his Ph.D., Hamidreza completed his Master Degree in Computer Engineering field of Artificial Intelligence at the University of Isfahan. His thesis entitled “Face Recognition Using Single Normal Reference Image and Feature Statistics" was written under the supervision of Amirhassan Monadjemi. Besides, he worked on Middle size soccer robot and Humanoid robot and obtained different ranks in RoboCup competition. The current version of his CV is available here, and his full list of publications and corresponding BibTeX files can be found on his Google scholar account.

Latest News

  • November 2017: Our paper Perceiving, Learning, and Recognizing 3D Objects: An Approach to Cognitive Service Robots got accepted at AAAI2018.
  • June 2017: A free and open source implementation of the GOOD descriptor is now available on my github account.
  • May 2017: Restaurant Object Dataset v.1.0 (RGB-D) is now available here! It contains 306 views of one instance of each category (Bottle, Bowl, Flask, Fork, Knife, Mug, Plate, Spoon, Teapot, and Vase), and 31 views of Unknown objects views (e.g. views that belong to the furniture).
  • April 2017: New journal paper accepted at Neurocomputing journal: Towards Lifelong Assistive Robotics: A Tight Coupling between Object Perception and Manipulation.
  • Jan 2017: I will be a research intern at ICVL Lab, Imperial Colledge London, UK.


My research interests focus on the intersection of robotics, machine learning and machine vision. I am interested in developing algorithms for an adaptive object perception system based on environment exploration and open-ended learning, which enables robots to learn from past experiences and interact with human users. My research is summarized by the following projects:

GOOD: A Global Orthographic Object Descriptor for 3D Object Recognition and Manipulation

Global object description is one of the most challenging tasks in robotics because it must provide reliable information to enable the robot to interact with the environment. This work presents a robust object descriptor named Global Orthographic Object Descriptor (GOOD) that provides a good trade-off among descriptiveness, computation time and memory usage, allowing concurrent object recognition and pose estimation. A video of this project is available at: []

Towards Lifelong Assistive Robotics: A Tight Coupling between Object Perception and Manipulation

In this work, we propose a cognitive architecture designed to create a tight coupling between perception and manipulation for assistive robots. This is necessary for assistive robots, not only to perform manipulation tasks in reasonable amounts of time and in appropriate manners, but also to robustly adapt to new environments by handling new objects. In particular, this cognitive architecture provides automatic perception capabilities that will allow robots to, (i) incrementally learn object categories from the set of accumulated experiences and (ii) reason about how to behave in response to complex goals. A video of this project is available at: []

Interactive Open-Ended Learning for 3D Object Recognition: An Approach and Experiments

This work presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this work, “open-ended” implies that the set of object categories to be learned is not known in advance. The training instances are extracted from on-line experiences of a robot, and thus become gradually available over time, rather than at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (i) How to automatically detect, conceptualize and recognize objects in 3D scenes in an open-ended manner? (ii) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? A video of this project is available at: []

Concurrent 3D Object Category Learning and Recognition based on Topic Modelling and Human Feedback

Topic modeling approaches usually construct the topics from a training set to recognize objects. However, in open-ended domains, the data available for training increases continuously. If limited training data is used, this might lead to non-discriminative topics and, as a consequence, to poor object recognition performance. This work proposes an object recognition system capable of learning object categories as well as the topics used to encode objects concurrently and in an open-ended manner. This system provides a robot with the capabilities to, (i) use unsupervised object exploration to construct a dictionary of visual words for representing objects and (ii) conceptualize object experiences and learn new object categories using topic modeling and human feedback. A video of this project is available at: []


Articles in international journals listed in the ISI

Other articles in journals

Chapters in books

Articles in conference proceedings