Master thesis proposals - external

Volumental

Evaluate and improve camera pose estimation

Accurate camera pose estimation is a necessity in many practical applications such as VR, autonomous cars, 3D Reconstruction and so on. For some appli- cations accuracy is more important than real-time camera tracking. ARKit is Apple’s tool that can estimate among other things the camera pose using the camera and the IMU. The question is how good does this work? At Volumental we are currently developing an app for scanning feet and we rely on the camera pose that is given by ARKit. In the Master Thesis project we aim to evaluate ARKit and improve it by creating a novel benchmark and derive a new algorithm.

Volumental is a computer vision company from RPL, KTH active in 3D body scanning and product recommendation based on 3D measurements in footwear. Today we have our computer vision systems are deployed across 32 countries and scanning hundreds of thousands of people regularly, working with some of the world’s biggest brands. We are a relatively small but growing team of PhDs in computer vision and machine learning and are product RPL-alumni. We are almost half women and hail from 9 different countries, we are located centrally at T-Centralen.

PDF-version of this announcement

Contact: Erik Bylow, erik.bylow@volumental.com

Ericsson

In an automated warehouse where autonomous robots load trucks with products while sharing the same environment with humans, a proper safety analysis is performed to avoid the hazardous situations without compromising the productivity. We have a basic safety analysis mechanism using image processing for object identification and risk assessment and mitigation (using fuzzy logic, RL approaches) to provide safety. The tasks of this thesis will be to extend the safety model: (1) adding safety from communication perspective i.e. among robots and between the robot and the edge / cloud; (2) performing highly-computational processing in edge / cloud; (3) looking into the Explainability of the used AI (XAI) techniques for Trustworthiness perspective; (4) model compression to run complex deep learning architectures in devices with limited hardware.
The high-level task is to perform safety analysis and image processing in the cloud and locally at the attached hardware and comparing both with respect to safety, performance, energy usage in human-robot collaborative use case.

Required qualifications:  MSc studies in Computer Science, Electrical and Computer Engineering or similar area; Excellent programming skills in C/C++, or Python or Java or Matlab; Good knowledge of concepts in machine learning (e.g. deep learning), robotics, ROS, etc.; Experiences with machine learning libraries Tensor flow, Keras, PyTorch, sci-kit learn etc.; Knowledge of safety analysis techniques, XAI, communication protocols and technologies (edge/cloud, D2D) is a bonus; Like to build end to end prototypes and concepts.

Contact: Rafia Inam (rafia.inam@ericsson.com), Alberto Hata (alberto.hata@ericsson.com )

Full project description here 

H&M

These projects will be performed in collaboration with H&M and aim to provide efficient solutions for some of the problems that the company is facing. The successful applicant will have an opportunity to closely interact with experts from H&M, have access to the real-world data owned by the company, and receive guidance from KTH researchers.

Project 1: Quality prediction

Every day, H&M orders, transports, and sells thousands of items. Quality control is an important part of this process. When boxes with goods are delivered to one of the warehouses, random quality checks are performed. There are multiple factors that can potentially affect the quality of the products in a specific box: they come different suppliers overseas, are transported in different ways (by ships, trains, or planes), and indirect factors such as weather also may play a certain role.

In this project, we aim to develop a probabilistic model that will predict the quality of the products based on the information about the supplier, the means of transportation, etc. To train the model, H&M will provide the relevant data. If implemented successfully, the system could later be used to identify the boxes that are more likely to require quality control.

Required qualifications: proficiency in Machine Leaning and Data Science. Applicants are expected to have passed KTH courses such as Advanced Machine Learning, Project Course in Data Science, Probabilistic Graphical Models, or equivalent.

Contact person: Anastasiia Varava (KTH) varava@kth.se

Project 2: Carton fillrate optimization

To transport the products, H&M needs to make decisions on how to package them. Depending on the characteristics of the products being packed (type of product, size, material, fragility, etc), different types of cartons can be used, and different number of items are packed into each of them.

Previously, this decisions were made by humans on a case-to-case basis.

In this project, we aim to automate this process. Based on the data collected earlier at H&M (item descriptions and packaging specifications), we aim to develop a model that will find the best way to package novel products. The model will need to capture specific features of the products that can potentially affect these decisions, and thus can be trained on existing data that will be provided by the company.

Required qualifications: proficiency in Machine Leaning and Data Science. Applicants are expected to have passed KTH courses such as Advanced Machine Learning, Project Course in Data Science, Probabilistic Graphical Models, or equivalent.

Contact person: Anastasiia Varava (KTH) varava@kth.se

Project 3: Inbound leadtime prediction

H&M orders large amounts of goods from multiple different suppliers in various countries to markets all over the world. Each product is expected to be delivered by a specific date. However, delays in production and transportation are inevitable, especially when the products are transported by ship due to multiple factors such as weather conditions, scheduling imprecision, multiple connections between different routes, etc. The goal of this project is to develop a probabilistic model that will be trained on the historical data provided by the company to predict the expected time needed for the goods to be manufactured and delivered to markets.

Required qualifications: proficiency in Machine Leaning and Data Science. Applicants are expected to have passed KTH courses such as Advanced Machine Learning, Project Course in Data Science, Probabilistic Graphical Models, or equivalent.

Contact person: Anastasiia Varava (KTH) varava@kth.se

Reinforcement Learning at Flexible Alternating Current Transmission Systems

You will be part of the Business Unit Grid Integration, located in Västerås. FACTS (Flexible Alternating Current Transmission Systems) technologies provide more power and control in existing AC as well as green-field networks and have minimal environmental impact. With a complete portfolio and in-house manufacturing of key components, ABB is a reliable partner in shaping the grid of the future. Please find out more about our world leading technology at www.abb.com/facts.

Problem description

At FACTS you will aid in creating a more sustainable future. The increase of available measurements, growth of real time processing capacity and communication abilities is changing the opportunities in the power system landscape. There is a potential to utilize more measurement data, Reinforcement Learning and a FACTS device for optimization purposes.

The task would include: 
• Create suitable test networks
• Investigate and develop prototype algorithm based on latest advancements in deep Reinforcement Learning
• Train, tune and test the algorithms on the networks and demonstrate policy optimality

Requirements

We are looking for you who are studying a university master program within a relevant technical area along with an interest within artificial intelligence or Reinforcement Learning. Experience in Reinforcement Learning or programming is positive. We aim to start the thesis in early September.