Master thesis proposals - external

ABB Robotics Automatic Learning of Behavior Trees for Industrial Robotics Today, industrial robots can solve very complex tasks in controlled environments, but modern industrial applications in workspaces shared with humans, require robots to be able to operate in unpredictable surroundings as well. One common way to deal with this is to control the robot with a reactive policy such as Behavior Trees (BTs). Also, with increased variation and smaller production batches, the time needed for programming, system integration, validation and verification is prohibitively long. It is therefore desirable that new robot policies or programs can be created automatically.¶

One way to generate policies automatically is machine learning algorithms. A drawback is that many of the most efficient machine learning algorithms, often based on Reinforcement Learning, are adapted to neural networks that have several important disadvantages compared to BTs and other model representations, as neural networks are not particularly transparent nor as modular.¶

Start: January 2023Duration: 6 monthsPlace: ABB Robotics (Västerås)ABB will cover the accommodation in VästeråsContact: Jonathan StyrudDetails here.¶

SCANIA Autonomous Transport Solutions Autonomous Transport Solutions (ATS) Research at Scania is responsible for developing, testing, and piloting future frontier ATS concepts. This work is done using agile and self-steered teams with the ambition to detect and evaluate upcoming technologies and prepare these for industrialization. We work in close cooperation with Volkswagen Group Innovation, leading technology suppliers, and academic institutions. As a thesis student, you'll get the opportunity to work closely with the members of a multicultural team where your ideas will be encouraged and embraced. Within ATS, we offer the following thesis proposals:

* 3D Semantic Mapping using Camera-LiDAR fusion. Apply here. Planned start: Jan 2023. Contact: Ajinkya Khoche (khoche@kth.se)
* Tracking and Predicting Hidden Traffic Participants for Safe Autonomous Driving. Apply here. Planned start: Jan 2023. Contact: Truls Nyberg (trulsny@kth.se)
* Safe and Efficient Occlusion-aware Decision Making for Autonomous Vehicles. Apply here. Planned start: Jan 2023. Contact: Truls Nyberg (trulsny@kth.se)
* Shared Prediction of Vulnerable Road Users in using V2X Communication for Autonomous Driving. Apply here. Planned start: Jan 2023. Contact: Vandana Narri (narri@kth.se)
SCANIA Smart Factory Lab Scania’s Global Industrial Development has a Smart Factory Lab Team where new techniques are adapted, evaluated, demonstrated and implemented. Within this area we have a number of possible topic proposals:

* AI for Human Well-Being ( Human Pose Estimation / Motion Capture)
* AI for Computer vision (Generate synthetics training data for production/Object detection for quality inspection)
* Two autonomous mobile robots exchange material

* Modern navigation and planning of mobile robots (with ROS)
* AI for improving Energy Consumption and System Performance in Production
We are looking for a number of students to collaborate with us. Do you have another idea? Projects may also be initiated by you, so we welcome suggestions. Please indicate your interest area in the cover letter.

Contact person: Xiaomeng Zhu (xiaomeng.zhu@scania.com)

More details and Apple here: 30 credits – Automation and Digitalization - Smart Factory Lab

Hitachi Energy The electrical power system is evolving due to e.g. an increasing amount of renewables and distributed power generation. FACTS (Flexible AC Transmission Systems) devices can assist with this evolution by improving the power quality and reducing the risk of system disconnections.

In this master thesis proposal, we want to investigate the application of machine learning to a FACTS device. The device itself is based on a power electronic converter and has its own local controller. The performance, in terms of efficiency and stability, is dependent on modelling assumptions which may to a certain extent deviate from reality. Here, the objective would be to improve the performance of the FACTS device, by application and integration of a suitable machine learning method into the control. The master thesis is planned to start in January 2023.

Contact: Jonathan Hanning, jonathan.hanning@hitachienergy.com

For more details, see: https://www.hitachienergy.com/career/jobs/details/SE53908798_E1

Ericsson Research Looking for a thesis project during Spring, starting in January 20223? Did you know Ericsson develops research on themes related to robotics and AI? Take a look at the current projects we have open:

Project 1: Assisted teleoperation

5G and 6G technologies are pushing boundaries of robot control over network connections. The purpose of this project is to apply concepts of state-of-the-art model-based control methods to robots being operated remotely. The goal is to assist the user and provide safety guarantees in the presence of dynamic obstacles, communication delay and possible packet loss. The project can be performed in simulation and/or on real hardware. Applicants should have a solid background in control theory (such as MPC and control barrier functions) and robotics.

Project 2: Collaborative SLAM

In this project you will investigate optimal strategies for collaborative simultaneous localization and mapping (SLAM) using modern smoothing and mapping techniques with graphs as the underlying structure. In particular, the goal is to investigate how collaborative SLAM can be performed in such a way that resource-constrained robots can achieve high performance, while minimizing the computational burden. Applicants should have a solid background in SLAM and robotics.

Project 3: Topology-Aware Robotic Exploration

Autonomous exploration algorithms allow robots to discover and map an environment. However, state-of-the-art approaches are information greedy and often give disorganized motion patterns. To increase efficiency, this project will look into exploiting knowledge about the topology of the environment, which can be used to give priorities to certain regions. Applicants should have a solid background in robotics and algorithms, including simulation tools like ROS and Gazebo.

Project 4: Edge-Assisted Object Detection

In this project you will work with an existing testbed for distributed object detection, where the computations can be offloaded from devices to edge servers. This requires a scheduler, that decides what computations to run and where. In one sub-project we will study a deep learning-based method to improve the performance of the current scheduler, along with exploring the impact of some network metrics on the offloading strategy. In another sub-project, we will study the effect of video content on the object detection performance and the offloading strategy. Applicants should have a solid background in ROS, computer vision and/or deep learning.

Requirements

All the projects require some common skills:

* Strong ability to formulate problems and solve them, independently and in groups.
* Programming skills, preferably in Python or C/C++.
* Strong communication skills in written and spoken English.
To apply or get more information

Project 1: Roberto Castro Sundin - roberto.castro.sundin@ericsson.com

Project 2: David Umsonst - david.umsonst@ericsson.com

Project 3: Fernando dos Santos Barbosa - fernando.dos.santos.barbosa@ericsson.com

Project 4: Alejandra Hernández Silva - alejandra.hernandez.silva@ericsson.com

Ocean Infinitiy Ocean Infinity has a number of proposals for thesis projects during Fall, starting in the end of August 2022. These involve problems within underwater perception, sonar and robotics. For example:

* Target Identification and classification from sidescan sonar data.
* Marine Growth assessment using computer vision, (Marine Growth Classification)
* MultiBeam Echo Sounder (MBES) image alignment (Multibeam Sonar Image Alignment)
* Image quality assessment (visibility, exposure, lighting, positioning etc) (Automated Image Assessment)
* Localization of AUV swarm (SLAM loop closing)
Please contact John Folkessn johnf@kth.se for more information.

Arriver Sweden Knowledge Distillation for Autonomous Driving

Description

CNN-based association for Multiple Object Tracking

Description

Univrses Cross-city domain shift is the cause of a 25-30% metrics drop of normal Deep Learning predictors. Change of weather and lighting conditions might even result in more disruptive effects. Therefore, to provide a robust and accurate output, the predictor would need to be trained on every possible deployment condition, e.g. multiple cites, districts, seasons, kinds of weather, cameras pose and intrinsics. This largely reduces the scalability of AI solutions both in terms of costs and deployment time. Recent Unsupervised Domain Adaptation (UDA) methods proved to be very effective in mitigating (or even solving) these shortcomings.

Contact: Pier Luigi Dovesi <pier.luigi@univrses.com>Application link, full description and details: https://career.univrses.com/jobs/1401972-master-thesis-project-unsupervised-domain-adaptation

ABB Corporate Research Using dialogue to disambiguate robot manipulation, learning from demonstration.Behavior Trees are a reactive task switching policy representation, used to control robotic agents. LfD can be used to teach the robot a task and BTs can be generated out of human demonstrations. However, ambiguities might rise if the target object for the task is similar to other objects in the environment. Thus, verbal-HRI can be used to disambiguate the task, both during the learning step and the execution step. The objective of the project is then to build a framework for continuous interaction between human and robot, from learning a task to execute it.Start: between Jan. 2022 and June 2022Duration: 6 monthsPlace: ABB CRC (Västerås)ABB will cover the accommodation in VästeråsContact: Matteo Iovino, matteo.Iovino@se.abb.comDetails here.

SkyCraft AB Master projects in CV and ML on powerline detection

Description

Viscando AB Projects within deep learning, signal processing and modelling for traffic and autonomous vehicle safety

Description

Babyshop Group Leveraging transfer learning for multi-label attribute prediction in fashion images, using deep learning

At Babyshop Group, there exist tens of thousands of images of children’s apparel. Currently, new items are labeled by hand, where each item has multiple attributes, eg. color, category, pattern, neckline, and style, etc. Since the workload of this manual task is immense, a need for a support system is high. In literature, a multitude of previous work has been done in regards to multi-label attribute prediction on fashion clothing, most notably DeepFashion and iMaterialist Fashion. Here, deep learning models have been trained on hundreds of thousands of labeled images to predict corresponding attributes, which has proven highly successful. However, almost all of these images are of adult fashion, with very little emphasis on childrens’ fashion. The question then arises, is it possible to combine the pre-trained models from previously mentioned multi-label attribution prediction models that are trained on almost exclusively adults’ fashion, together with the learnings from transfer-learning frameworks - in order to successfully perform multi-label attribution prediction on childrens’ fashion?

Required qualifications: MSc studies in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or related field; Good understanding of machine learning frameworks such as Keras, TensorFlow, PyTorch, Scikit-Learn, and/or Spark; Proficiency in Python and Git; Knowledge about data wrangling and data munging, using SQL, Pandas, and Numpy.

Web version of the proposal with instructions on how to apply.

Time frame: from early January to mid-June.

Contact: Marcus Svensson (marcus.svensson@babyshop.se), Data Scientist at Babyshop Group