Perception and Manipulation

Dual-arm Manipulation

Neural Motion Planning

Latest News

Sep. 2021: Hamidreza Kasaei is serving as associate editor for the IEEE/RSJ ICRA 2022! .
July 2021: We will organize a full-day workshop on 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeruIPS2021.
July. 2021: Our paper titled The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation got accepted to Journal of Intelligent & Robotic Systems
May. 2021: Our paper titled MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation got accepted to 30th IEEE International Conference on Robot and Human Interactive Communication (RO_MAN 2021)! Congrats Tommaso!
June. 2021: Hamidreza Kasaei gave an invited talk at the University of Groningen on Towards lifelong robot-learning in human-centric environments: How robots can adapt to new environments incrementally?
May. 2021: Our paper titled Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces got accepted to IEEE-RAS International Conference on Humanoid Robots (Humanoids2020)! Congrats Nils!
April. 2021: We are looking for a motivated Ph.D. student to work on "Dual-arm manipulation tasks". Detailed information about how to apply is available here!
April. 2021: Our paper titled 3D_DEN: Open-ended 3D Object Recognition Using Dynamically Expandable Networks got accepted to IEEE Transactions on Cognitive and Developmental Systems! Congrats Sudhakaran!
March. 2021: Our paper titled Self-Imitation Learning by Planning got accepted to ICRA2021! Congrats Sha!
Feb. 2021: Hamidreza Kasaei is serving as associate editor for the IEEE/RSJ IROS 2021!
Feb. 2021: Hamidreza Kasaei gave an invited talk at the Bosch Center for Artificial Intelligence (BCAI) on Robots Beyond the Factory: Open-ended Robot Learning in Human-Centric Environments!
Jan. 2021: Our paper titled Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition got accepted to Intelligent Service Robotics Journal!
Nov. 2020: Our paper titled OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains got accepted to IEEE Transactions on Mechatronics! (IF 5.71).
October 2020: Hamidreza Kasaei is serving as associate editor for the IEEE/RSJ ICRA 2021! .
June 2020: Our paper titled Learning to Grasp 3D Objects using Deep Residual U-Nets got accepted to IEEE RO-MAN 2020! Congrats Yikun!.
March 2020: Our paper titled Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning got accepted to IJCNN 2020! Congrats Sha!
October 2019: Hamidreza Kasaei is serving as associate editor for the IEEE/RSJ ICRA 2020! .
June 2019: Our paper Local-LDA: Open-Ended Learning of Latent Topics for 3D Object Recognition got accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI - IF = 17.730).
June 2019: Our paper Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition got accepted at IROS 2019.
May 2019: We will organize a full-day workshop on Task-Informed Grasping (TIG-II): From Perception to Physical Interaction at RSS2019.
April 2019: We will organize a full-day workshop on Open-Ended Learning for Object Perception and Grasping: Current Successes and Future Challenges at IROS 2019.
April 2019: Hamidreza Kasaei got selected to be a member of the 2019 cohort of the RSS Pioneers.
January 2019: Our paper Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation got accepted at ICRA 2019.
October 2018: Our NVIDIA GPU Grant has been approved. Thank NVIDIA Corporation for supporting our works.

Research Directions

In our group, we focus on Lifelong Interactive Robot Learning to make robots capable of learning in an open-ended fashion by interacting with non-expert human users. In particular, we have been developing this goal over the following six specific research directions. Details of our publications, including paper, demonstration, and code, can be found here!

Robot Perception and Perceptual Learning

We are interested in attaining a 3D understanding of the world around us. In particular, the perception system provides important information that the robot has to use for interacting with users and environments.

Object Grasping and Object Manipulation

A service robot must be able to interact with the environment as well as human users. We are interested in fundamental research in object-agnostic grasping, affordance detection, task-informed grasping, and object manipulation.

Lifelong Interactive Robot Learning

No matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, the robot should be able to continually learn about new tasks from few training examples on-site by interacting with non-expert users.

Dynamic Robot Motion Planning

We are interested in attaining fully reactive manipulation functionalities in a closed-loop manner. Reactive systems have to continuously check if they are at risk of colliding while planners should check every configuration that the robot may attempt to use.

Exploiting Multimodality

A service robot may sense the world through different modalities that may provide visual, haptic or auditory cues about the environment. In this vein, we are interested in exploiting multimodality for learning better representations to improve robot's performance.

Dual-Arm Manipulation

A dual-arm robot has very good manipulability and maneuverability which is necessary to accomplish a set of everyday tasks (dishwashing, hammering). We are interested in efficient imitation learning, collabrative manipulation, and large object manipulation.

People

Open Positions

We are actively looking for students to work on several amazing projects that involve:

  • Deep learning-based method for 3D object perception.
  • Visual representation learning for physical interaction (grasping and manipulation).
  • Deep reinforcement and imitation learning-based methods for planning & control.
  • Dual-arm object manipulation.
  • Lifelong Robot learning.
  • Multi-Task multimodal learning.
  • Simulation to real-world transfer learning (Sim2Real).

If you are interested in doing your PhD/Master/Bachelor thesis in one of the mentioned Research Directions, please send us an e-mail including the following information:

  • Short CV
  • Short motivation letter

The motivation letter should state (½ - 1 page):

  • Topics that you are interested in
  • Type of project (theoretical/applied)
  • Intended starting date
  • Your relevant experiences

We will try to get back to you within a week. The primary qualification is interest in doing research, and if you have that, we encourage you to apply even if you have limited experience with AI. We strive to be a diverse and inclusive group that is open to students from all backgrounds.

PhD position in lifelong learning for dual-arm manipulation tasks!

We are looking for a motivated Ph.D. student to work on "Dual-arm manipulation tasks". This Ph.D. project focuses on learning dual-arm manipulation skills for service robots to assist humans in various household/industrial tasks. The position is fully funded for four years. The intended starting date is on 1 November 2021, but different starting dates can be considered. Detailed information about how to apply is available here!

Seld-funded (externally funded) PhD, Master and Visiting Scholar

Good self-founded PhD, Master, and intern will be considered on a case by case basis. If you are interested in doing your PhD at Interactive Robot Learning Lab and you have founding from your Government (e.g., China Scholarship Council (CSC) scholarship), we can consider your application. Please contact hamidreza.kasaei@rug.nl

Students Projects

PhD Projects

  • Sha Luo (Oct.2018 ~ )
  • Deep Reinforcement Learning for Flexible Visually Guided Grasping
  • Advisors: Lambert Schomaker , Hamidreza Kasaei

Master Projects

  • Jos van Goor (Feb.2021 ~ )
  • Leveraging Deep Object Recognition Models for Per-Point 6-DOF Grasp Synthesis
  • Krishna Santhakumar (Feb.2021 ~ Sep.2021 )
  • Lifelong Object Grasp Synthesis using Dual Memory Recurrent Self-Organization Networks
  • Arjan Jawahier (March.2021 ~ )
  • Descriptive Viewpoint Prediction: Simultaneous Object Recognition and Grasping in Service Robots
  • Hari Vidharth (Feb.2021 ~ )
  • Learning and Generalization of Long-Horizon Sequential Pick and Place Tasks with Deep Reinforcement Learning
  • Georgios Tziafas (Feb.2021 ~ Sep.2021 )
  • Sim2Real Transfer of Visiolinguistic Representations for Human-Robot Interaction
Paper Demo
  • Thijs Eker (Nov.2020 ~ Sep.2021) [Internship at TNO]
  • Viewpoint-invariant Ship Classification using 3D Reconstruction Models
  • Tommaso Parisotto (July.2020 ~ March 2021)
  • MORE: Simultaneous Multi-View 3D Object Recognition and Grasp Pose Estimation
Paper Code Thesis
  • Sudhakaran Jain (March.2020 ~ Dec.2020)
  • Open-Ended 3D Object Recognition using Dynamically Evolving Neural Networks
Paper Demo Code Thesis
  • Subilal Vattimunda Purayil (April.2020 ~ Dec.2020)
  • Learning Deep Spatio-Temporal Features for Human Activity Classification
Thesis
  • Diego Cabo Golvano (Feb.2020 ~ Dec.2020)
  • Exploring Novel Hierarchical Reinforcement Learning Approaches to Lifelong Learning
Thesis
  • Yikun Li (Jan.2019 ~ Aug.2019)
  • Learning to Detect Grasp Affordances of 3D Objects using Deep Convolutional Neural Networks
Paper Demo Dataset Thesis
  • Mario Rios-Munoz (Jan.2019 ~ Mar. 2020)
  • Learning to Grasp: A Deep Learning Approach to Generalized Robust Grasp Affordance
Paper Thesis Code

Undergraduate Projects

  • Jeroen Oude Vrielink (Feb.2021 ~ )
  • Learning grasp affordances of 3D objects using Deep Convolutional Neural Networks
Proposal Thesis Page Demo Code
  • Anne-Jan Mein (Feb.2021 ~ )
  • Investigating the influences of different colour spaces in open-ended 3D recognition
Proposal Thesis Page
  • Junhyung Jo (Feb.2021 ~ )
  • Fine-grained 3D object recognition: an approach and experiments
Proposal Thesis Page
  • Jim Wu (Sep.2020 ~ Jan.2021 )
  • Lifelong 3D Object Recognition: a comparison of deep features and handcrafted descriptors
Thesis Page
  • Andreea Toca (Feb.2020 ~ July 2020)
  • Investigating the Importance of Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition
Proposal Thesis Page
  • Vlad Iftime (Feb.2020 ~ July 2020)
  • Autoencoder-based Representation Learning for 3D Object Recognition in Open-Ended Domains
Proposal Thesis Page
  • Roberto Navarro (Feb.2020 ~ July 2020)
  • Learning to Grasp 3D objects using Deep Convolutional Neural Network
Proposal Thesis Page
  • Nils Keunecke (Feb.2020 ~ July 2020)
  • 3D Object Recognition using OrthographicNet and Color Constancy
Proposal Thesis Page Demo
  • Sandra Bedrossian (Jan2019 ~ July2019)
  • Vehicle License Plate Recognition using Pixel Information
Proposal Thesis Page

Teaching

Contact



Dr. Hamidreza Kasaei
Artificial Intelligence Department,
University of Groningen,
Bernoulliborg building,
Nijenborgh 9 9747 AG Groningen,
The Netherlands.
Office: 340
Tel: +31-50-363-33926
E-mail: hamidreza.kasaei@rug.nl