Start date: Open

Location: Leoben

Position Types: Thesis/Internship

Duration: 3-6 months, depending on the level of applicant’s proficiency on the asked qualifications.

Keywords: Human-Robot Interaction (HRI), Human Gesture Recognition, Sign Language, Robotics, Computer Vision, Large Language Models (LLMs), Behavior Cloning, Reinforcement Learning, Digital Twin, ROS-2

Supervisor:

• Fotios Lygerakis

You can work on this project either by doing a B.Sc or M.Sc. thesis or an internship*.

Abstract

As the interaction with robots becomes an integral part of our daily lives, there is an escalating need for more human-like communication methods with these machines. This surge in robotic integration demands innovative approaches to ensure seamless and intuitive communication. Incorporating sign language, a powerful and unique form of communication predominantly used by the deaf and hard-of-hearing community, can be a pivotal step in this direction. 

By doing so, we not only provide an inclusive and accessible mode of interaction but also establish a non-verbal and non-intrusive way for everyone to engage with robots. This evolution in human-robot interaction will undoubtedly pave the way for more holistic and natural engagements in the future.

Project Description

The implementation of sign language in human-robot interaction will not only improve the user experience but will also advance the field of robotics and artificial intelligence.

This project will encompass 4 crucial elements.

1. Human Gesture Recognition with CNNs and/or Transformers – Recognizing human gestures in sign language through the development of deep learning methods utilizing a camera.
   • Letter-level
   • Word/Gloss-level
2. Chat Agent with Large Language Models (LLMs) – Developing a gloss chat agent.
3. Finger Spelling/Gloss gesture with Robot Hand/Arm-Hand –
   
   • Human Gesture Imitation
   • Behavior Cloning
   • Offline Reinforcement Learning
4. Software Engineering – Create a seamless human-robot interaction framework using sign language.
   • Develop a ROS-2 framework
   • Develop a robot digital twin on simulation
5. Human-Robot Interaction Evaluation – Evaluate and adopt the more human-like methods for more human-like interaction with a robotic signer.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, Mathematics or related fields.
• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience working with robotics hardware.
• Knowledge of computer vision and image processing techniques
• Strong problem-solving skills and ability to work independently and collaboratively.
• Good written and verbal communication skills in English.
• Passion for creating technology that is accessible and inclusive for everyone
• Experience in working on research projects or coursework related to robotics or artificial intelligence is a plus

Opportunities

This project provides an excellent opportunity to gain hands-on experience in cutting-edge research, working with a highly collaborative and supportive team. The student/intern will also have the opportunity to co-author research papers and technical reports, and participate in conferences and workshops.

Application/Questions

Send us your CV accompanied by a letter of motivation at fotios.lygerakis@unileoben.ac.at with the subject: “Internship/Thesis Application | Sign Language Robot Hand”

Funding

* This project does not offer a funded position. Below we list some relevant grant application details.

CEEPUS grant (European for undergrads and graduates)

Find details on the Central European Exchange Program for University Studies program at https://grants.at/en/ or at https://www.ceepus.info.

In principle, you can apply at any time for a scholarship. However, also your country of origin matters and there exist networks of several countries that have their own contingent.

Ernst Mach Grant (Worldwide for PhDs and Seniors)

Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.

Rest Funding Resourses

Apply online at http://www.scholarships.at/

Abstract

Human-Robot Interaction using Sign Language is a project that aims to revolutionize the way we communicate with machines. With the increasing use of robots in our daily lives, it is important to create a more natural and intuitive way for humans to communicate with them.

Sign language is a unique and powerful form of communication that is widely used by the deaf and hard-of-hearing community. By incorporating sign language into robot interaction, we can create a more inclusive and accessible technology for everyone.

Moreover, sign language will provide a new and innovative way to interact with robots, making it possible for people to control and communicate with them in a way that is both non-verbal and non-intrusive.

Note: This project is also offered as Internship position.

Thesis Description

The implementation of sign language in human-robot interaction will not only improve the user experience but will also advance the field of robotics and artificial intelligence. This project has the potential to bring about a new era of human-robot interaction, where machines and humans can communicate in a more natural and human-like way. Therefore, the Human-Robot Interaction using Sign Language project is a crucial step toward creating a more accessible and user-friendly technology for everyone.

This thesis will encompass three crucial elements. The first part will focus on recognizing human gestures in sign language through the development of deep learning methods utilizing a camera. The second part will involve programming a robotic hand to translate text back into gestures. Finally, the third part will bring together the first two components to create a seamless human-robot interaction framework using sign language.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, Mathematics or related fields.
• Strong programming skills in Python
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience working with robotics hardware
• Knowledge of computer vision and image processing techniques
• Good written and verbal communication skills in English.

Interested?

If this project sounds like fun to you, please contact Fotios Lygerakis by email at fotios.lygerakis@unileoben.ac.at or simple visit us at our chair in the Metallurgie building, 1st floor.

Meeting 01/12

Updates

• experiments with caltech 101
  • too small dataset. Network needs pretraining
  • too big bictures. problems with gpu memory when training and big storage space when saving models
• refactor code to better scale for more evaluation techniques
• reviewed XAI methods.
• Further literature review for representation learning

MS Student Updates

• Melanie
  • Image Segmentation on Steel Defect dataset
  • Next on: Deep Optical Flow

Other activities

• Hololens 2 review
• plan to publish the AR project as internship position
  • LinkedIn -> CPS page?
  • MUL
  • Emails
• Share Christmas video with the public relations team of MUL
• linked in account ->  page

Next on

• do experiments for interpolating latent space
• run experiments with more datasets
• reconstruct the paper for
  • ICML
  • IJCAI
• literature review on representation learning
• seminar talk for latent space representation and explainability in neural networks, feature maps. organize meetings (1 paper per week)

Meeting 08/12

Updates

MS Student Updates

• Melanie
  •  
  • Next on:

Other activities

• PhD in Computer Science

Next on

Meeting 03/11​

Done

• Virtual machine setup & running experiments
  • speed is x3 slower than RTX 3090

Started working on

• develop experiments with new dataset (Caltech101)
• assess experiments
• test transfer learning capabilities of CR-VAE

MS Students Updates

• Melanie
  • Object Detection using Resnet
• Julian
  • basic tutorials on ROS
  • Teleoperation of Turtlebot using PS5 controller
  • UR3 on ROS using MoveIt

Next on

• description of caltech
• talk with Konrad about the cluster
• Update paper with correct results and send it to CVPR
• Hyperparameter grid search experiments
• literature review on representation learning
• experiments with artificial datasets
• develop methods
  • contrastive learning for spiking neural networks.
  • mode-seeking kl divergence

Meeting 11/11​

Progress

• Updated draft of the CR-VAE paper.
• Missed author registration deadline for CVPR >_<*
• Run experiments with simple AE / CR-AE
• Test if InfoMax objective actually works -> it doesn’t.
• New implementation of the loss function
• Tried input normalization and MSE for reconstruction error

• Assess experiments
• Hyperparameter grid search experiments for CR-VAE
• Literature review on representation learning (review paper)
• Going through ROS 2 documentation
• Get acquanted with UR3

MS Students Updates

• Melanie
  • Object Detection using Resnet
  • Next on: Image Segmentation
• Julian
  • Teleoperation of Turtlebot using PS5 controller
  • Next on: UR3 on Gazebo using MoveIt

Other activities

• Discussion with Sahar and Vedant about how Sahar could frame her Reinforcement learning research problem.

Next on

• assess experiments from grid search with the new loss function
  • hopefully there will be some distinct difference of the 3 methods
• further assess the value of MI as an auxilary task for unsupervised representation learnning
  • show that MI in InfoMax actually introduce noise
• Denoising AE/CR-AE/VAE/CR-VAE with augmented images.
• develop experiments with new dataset (Caltech101)
• description of caltech
• test transfer learning capabilities of CR-VAE
• literature review on representation learning
• experiments with artificial datasets
• develop methods
  • contrastive learning for spiking neural networks.
  • mode-seeking kl divergence

   

Journal ideas

• Find the best CL method for CR-VAE
• Transfer learning

Meeting 17/11

Progress

• gpu grid setbacks
• caltech101 dataset
• ROS2 refresh
• new results

MS Students Updates

• Melanie
  • Image Segmentation on Steel Defect dataset
  • Next on: Contrastive Learning
• Julian
  • Sick
  • Next on: UR3 on Gazebo using MoveIt

Other activities

• Study abroad fair speech

Next on

• study the big performance gap in KLD between CR-VAE and VAE
• further assess the value of MI as an auxilary task for unsupervised representation learnning
  • show that MI in InfoMax actually introduce noise
• Denoising AE/CR-AE/VAE/CR-VAE with augmented images.
• develop experiments with new dataset (Caltech101)
• description of caltech
• test transfer learning capabilities of CR-VAE
• literature review on representation learning
• experiments with artificial datasets
• develop methods
  • contrastive learning for spiking neural networks.
  • mode-seeking kl divergence

   

Journal ideas

• Find the best CL method for CR-VAE
• Transfer learning
• develop a contrastive regularization layer for NN

Meeting 23/11

Updates

• AAAI paper submission update
  • received an email that the file was never uploaded even though I have a verification email. Still in the process of figuring out
• new results on smaller architecture -> more distinct results

MS Students Updates

• Melanie
  • Image Segmentation on Steel Defect dataset
  • Next on: Contrastive Learning
• Julian
  • Dropped
  • Subject was not aligned with his program
  • working at the lab did not fit his schedule

Other activities

• christmas & hololens 2 unboxing videos
• storage place or display for PS5 controler, hololense, etc?
• plan to publish the AR project as internship position
  • LinkedIn -> CPS page?
  • MUL
  • Emails

Next on

• reconstruct the paper
  • ICML
  • IJCAI
• caltech101 dataset
• literature review on representation learning
• Hololens 2 review
• seminar talk for latent space representation and explainability in neural networks, feature maps. organize meetings (1 paper per week)

Meeting 30/11​

Updates

• experiments with caltech 101
  • too small dataset. Network needs pretraining
  • too big bictures. problems with gpu memory when training and big storage space when saving models
• refactor code to better scale for more evaluation techniques
• reviewed XAI methods.
• Further literature review for representation learning

MS Student Updates

• Melanie
  • Image Segmentation on Steel Defect dataset
  • Next on: Deep Optical Flow

Other activities

• Hololens 2 review
• plan to publish the AR project as internship position
  • LinkedIn -> CPS page?
  • MUL
  • Emails
• Share Christmas video with the public relations team of MUL
• linked in account ->  page

Next on

• do experiments for interpolating latent space
• run experiments with more datasets
• reconstruct the paper for
  • ICML
  • IJCAI
• literature review on representation learning
• seminar talk for latent space representation and explainability in neural networks, feature maps. organize meetings (1 paper per week)

Meeting 21/10

Done

• experiment assessing with small custom architecture

Next on

• find a new controller
• set up computer for Melanie & Julian
• Virtual machine setup

Meeting 25/10

Done

• preliminary experiment assessing with resnet architecture
• schedule new experiments on resnet architecure
• preparation and meetings with MS studentsmeetings notes per month

Next on

• assess experiments
• literature review on representation learning
• Virtual machine setup
• experiments with artificial datasets
• develop methods
  • contrastive learning for spiking neural networks.
  • mode-seeking kl divergence

Increase your Productivity

Schedule your weekly tasks, meetings, courses or activities!

Increase your Flexibility

Access your files from any computer, tablet or phone!

Work as a Team

Edit together in real-time with easy sharing, and use comments, suggestions, and action items to keep things moving. Or use @-mentions to pull relevant people, files, and events into your online files for rich collaboration.

Important Links

• Calendar
• Documents(Google’s “Word”)
• Sheets(Google’s “Excel”)
• Slides (Google’s PowerPoint)
• Forms

Participants

Niko, Fotis, Linus, Vedant

Agenda

• First discussion on the projects structure of CPS Hub
• Initial plan & examples

Notes

• Components (e.g. UR3, RH8D_hand, Glove, Hololens2) are independent repositories
• Projects (e.g. TacProMPs, HololensTeleop) are independent repositories that use the above repos.
• No custom messages without previous team meeting
• Use Foxy ROS2

Start date: Open

Location: Leoben

Job Type: Internship

Duration: 3-6 months, depending on the level of applicant’s proficiency on the asked qualifications.

Keywords: Mixed Reality, Augmented Reality, Unity 3D, Robotic Manipulators, ROS 2, Hololens 2.

Supervisors:

• Fotios Lygerakis
  
• Univ. Prof. Dr Elmar Rückert

Job Description

The Mixed Reality (AR) interface for intuitive programming of robotic manipulators project is a unique opportunity to work on cutting-edge research in the field of robotics and mixed reality. This project aims to create an intuitive programming interface for robotic manipulators using Unity 3D and ROS 2 robotic framework.

By participating in this internship, you will gain valuable skills in mixed reality development, robotics, and software engineering, and contribute to a project with the potential to revolutionize the way we program and control robotic manipulators.

Note: This project is also offered under the Integrated CPS project course or as B.Sc or M.Sc. Thesis

Responsibilities

As an intern, you will work on developing a mixed reality (AR) interface using Unity 3D that enables intuitive programming of robotic manipulators. You will work on integrating this interface with the ROS 2 robotic framework to control a robotic manipulator (UR3).

This internship will give you hands-on experience in developing mixed reality applications and working with robotics hardware. You will collaborate with a supportive team of researchers and engineers to solve challenging problems in mixed reality and robotics. You will also have the opportunity to learn about the unique challenges and opportunities involved in creating innovative and intuitive interfaces for programming robotic systems.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering or related fields.
• Strong programming skills in C# and Unity 3D
• Familiarity with ROS or other robotic frameworks
• Knowledge of 3D modeling and animation
• Experience with mixed reality development and programming
• Good written and verbal communication skills in English.
• Passion for creating innovative and intuitive interfaces for programming robotic systems
• Experience in working on research projects or coursework related to robotics or mixed reality is a plus

Opportunities and Benefits of the Internship

This internship provides an excellent opportunity to gain hands-on experience in cutting-edge research on unsupervised learning for few-shot learning, working with a highly collaborative and supportive team. The intern will also have the opportunity to co-author research papers and technical reports, and participate in conferences and workshops.

Application

Send us your CV accompanied by a letter of motivation at fotios.lygerakis@unileoben.ac.at with the subject: “Internship Application | Mixed Reality Robot Teleoperation”

Funding

We will support you during your application for an internship grant. Below we list some relevant grant application details.

CEEPUS grant (European for undergrads and graduates)

Find details on the Central European Exchange Program for University Studies program at https://grants.at/en/ or at https://www.ceepus.info.

In principle, you can apply at any time for a scholarship. However, also your country of origin matters and there exist networks of several countries that have their own contingent.

Ernst Mach Grant (Worldwide for PhDs and Seniors)

Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.

Rest Funding Resourses

Apply online at http://www.scholarships.at/

Description

Mixed Reality (AR) interface based on Unity 3D for intuitive programming of robotic manipulators (UR3). The interface will be implemented within on the ROS 2 robotic framework.

Note: This project is also offered as Internship position.

Abstract

Robots will become a necessity for every business in the near future. Especially companies that rely heavily on the constant manipulation of objects will need to be able to constantly repurpose their robots to meet the ever changing demands. Furthermore, with the rise of Machine Learning, human collaborators or ” robot teachers” will need a more intuitive interface to communicate with them, either when interacting with them or when teaching them.

In this project we will develop a novel Mixed (Augmented) Reality Interface for teleoperating the UR3 robotic manipulator. For this purpose we will use AR glasses to augment the user’s reality with information about the robot and enable intuitive programming of the robot. The interface will be implemented on a ROS 2 framework for enhanced scalability and better integration potential to other devices.

Outcomes

This thesis will result to an innovative graphical interface that enables non-experts to program a robotic manipulator.

The student will get valuable experience in the Robot Operating System (ROS) framework and developing graphical interfaces on Unity. The student will also get a good understanding of robotic manipulators (like UR3) and develop a complete engineering project.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering or related fields.
• Good programming skills in C# and Unity 3D
• Familiarity with ROS or other robotic frameworks
• Basic knowledge of 3D modeling and animation
• Good written and verbal communication skills in English.
• (optional) Experience with mixed reality development and programming

Interested?

If this project sounds like fun to you, please contact Fotios Lygerakis by email at fotios.lygerakis@unileoben.ac.at or simple visit us at our chair in the Metallurgie building, 1st floor.

Agenda

Next steps after AAAI submission

Upcoming research questions to answer

1. Normalize total loss
2. What is the performance of CR-VAE with ResNet architecture on MNIST and CIFAR-10 Datasets?
3. What is the performance of MoCo on MNIST and CIFAR-10 Datasets?
4. How does CR-VAE-BIG compare with MoCo?
5. What is better, SGD or Adam? Why?
6. What is better, E2E or Modular? Why?
7. How can we train on ImageNet? Maybe alternative datasets?
8. New architecture: decoder input -> concatenated latent representations from q and k encoders.
9. Can we incorporate all representation techniques into one?

post paper submission setbacks

• KL divergence computation was wrong. When fixed, performance was different
• With a weight factor of 1 for the KL divergence, the learned features performance in classification task diminish.
• This report shows this problem.
• Same behavior for CR-VAE. Untill the reconstruction and the contrastive losses are in the same scale with the KLD loss, the performance will continue to deviate. This happens because KLD dominates numerically the total loss.
• Way to mitigate it:
  • Descending beta value
    • currently exploring different scheduling techniques
    • report
• Note: CR-VAE does not seem novel now
• Normalizing total loss (weight loss inversively with their magnitude) might lead to better performance