Summer internship on Docker containers for mobile robots

In the Robotics Lab, we have many mobile robots, including Spot, Care-o-Bot 4, and Husky. Each of our platforms has specific requirements for its development environment. Maintaining different versions of ROS and other libraries on the same machine is impractical and prone to conflicts. For this and other reasons, individual sandboxed development containers are preferable […]

Master thesis on “Development of data-driven driver model”

Supervisor: Prof. Ville Kyrki (ville.kyrki@aalto.fi) Advisor: Daulet Baimukashev  (daulet.baimukashev@aalto.fi), Shoaib Azam (shoaib.azam@aalto.fi) Keywords: imitation learning, autonomous driving Data-driven driver models are superior to rule-based models in interactive multi-agent scenarios where it is essential to consider agents’ behavior. For example, humans have diverse driving styles as aggressive, neutral, or defensive [1] and it is challenging to […]

Semantic map generation in SUMO

This project aims to extend the functionality of the SUMO simulator with suitable software packages which generate semantic representations and control the vehicles using low-level control actions. This enables integration of data-driven vehicle models.

Master Thesis on “Interactive Bayesian Multiobjective Evolutionary Optimization in Reinforcement Learning Problems with Conflicting Reward Functions”

In many real-world problems, there are multiple conflicting objective functions that need to be optimized simultaneously. For example, an investment company wants to create an optimum portfolio of stocks to maximize profits and minimize risk simultaneously. However, most reinforcement learning (RL) problems do not explicitly consider the tradeoff between multiple conflicting reward functions and assume a scalarized single objective reward function to be optimized. Multiobjective evolutionary optimization algorithms (MOEAs) can be used to find Pareto optimal policies by considering multiple reward functions as objectives.

Deformable Object Manipulation

In this project we research on how to manipulate more efficiently deformable objects by using dynamic manipulation as well as the modeling deformable objects via graph structures. Our applications range from manipulation of granular materials such as ground coffee to cloth manipulation.