UR3 passwords
Robot serial number:20225300304
Passwords:
- safety: 0000
1
Robot serial number:20225300304
Passwords:
Stability: Normalizing the representations ensures that they all have the same magnitude. This can make the learning process more stable, as it prevents the model from assigning arbitrarily large or small magnitudes to the representations.
Focus on direction: By constraining the representations to have a fixed magnitude, the learning process focuses on the direction of the vectors in the embedding space. This is often what we care about in tasks like contrastive learning, where the goal is to make the representations of similar inputs point in similar directions.
Computational convenience: As mentioned earlier, many computations, such as the dot product between two vectors, are easier to perform and interpret in normalized spaces.
Interpretability: Normalized representations are often more interpretable, as the angle between two vectors can be directly interpreted as a measure of similarity or dissimilarity.
Literature Review
ECAI review papers
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: Self-supervised learning, Few-shot learning, Deep learning, PyTorch, Research
Supervisors:
We are looking for a highly motivated research intern to work on the development of novel self-supervised learning algorithms to improve few-shot learning. The intern will be responsible for conducting research on self-supervised learning techniques such as contrastive learning and generative models, and their applications to few-shot learning. The intern will also be responsible for implementing and evaluating these algorithms on benchmark datasets.
This internship provides an excellent opportunity to gain hands-on experience in cutting-edge research on self-supervised 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.
Send us your CV accompanied by a letter of motivation at fotios.lygerakis@unileoben.ac.at with the subject: “Internship Application | Self-supervised Learning”
We will support you during your application for an internship grant. Below we list some relevant grant application details.
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.
Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.
Apply online at http://www.scholarships.at/
M.Sc. Students/Interns
Science Breakfast @MUL: 14/02 11:00-12:00
Anymal Robot at Mining chair on 15/02?
The need for efficient and compact representations of sensory data such as visual and textual has grown significantly due to the exponential growth in the size and complexity of the data. Self-supervised learning techniques, such as autoencoders, contrastive learning, and transformer, have shown significant promise in learning such representations from large unlabeled datasets. This research aims to develop novel self-supervised learning techniques inspired by these approaches to improve the quality and efficiency of unsupervised representation learning.
The study will begin by reviewing the state-of-the-art self-supervised learning techniques and their applications in various domains, including computer vision and natural language processing. Next, a set of experiments will be conducted to develop and evaluate the proposed techniques on standard datasets in these domains.
The experiments will focus on learning compact and efficient representations of sensory data using autoencoder-based techniques, contrastive learning, and transformer-based approaches. The performance of the proposed techniques will be evaluated based on their ability to improve the accuracy and efficiency of unsupervised representation learning tasks.
The research will also investigate the impact of different factors such as the choice of loss functions, model architecture, and hyperparameters on the performance of the proposed techniques. The insights gained from this study will help in developing guidelines for selecting appropriate self-supervised learning techniques for efficient and compact representation learning.
Overall, this research will contribute to the development of novel self-supervised learning techniques for efficient and compact representation learning of sensory data. The proposed techniques will have potential applications in various domains, including computer vision, natural language processing, and other sensory data analysis tasks.
If this topic excites you 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.
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:
You can work on this project either by doing a B.Sc or M.Sc. thesis or an internship*.
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.
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.
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.
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”
* This project does not offer a funded position. Below we list some relevant grant application details.
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.
Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.
Apply online at http://www.scholarships.at/