Summer Internships at the Intelligent Robotics Group

Every year the Intelligent Robotics Lab hires summer interns for a multitude of projects, ranging from infrastructure projects to advanced research topics. Two summer interns who recently finished their summer stay in Helsinki in 2023 are Tiia Tikkala and Sergio Hernández, both supervised by Dr. Kevin Sebastian Luck.

Explainable Interactions between Humans and Autonomous Systems

With the growing advancement of robotics research, there is a growing need for people-friendly communication between robots and humans. On one hand, the decisions of the autonomous system need to be understandable to humans, and on the other – humans need to be able to specify commands in a way that is natural to them. […]

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.

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.

Manipulation of Granular Materials by Learning Particle Interactions

In this work we propose to use a Graph Neural Network (GNN) surrogate model to learn the particle interactions of granular materials. We perform planning of manipulation trajectories with the learnt surrogate model to arrange the material into a desired configuration.