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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.
ICRA 2023, here we come!
The Intelligent Robotics group will attend to ICRA 2023. Read more!
Master thesis on “Development of data-driven driver model”
This thesis aims to develop data-driven driver models using expert data. Many real-world driving datasets with diverse driving scenarios and closed-loop evaluation frameworks are currently available from Waymo, NuPlan, and Lyft.
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.
Our work “Learning Visual Feedback Control for Dynamic Cloth Folding” was accepted to IROS 2022!
We are happy to announce that our work “Learning Visual Feedback Control for Dynamic Cloth Folding” was accepted to IROS 2022 and nomitated to both the IROS Best Paper award, Best Student Paper award and the IROS Best RoboCup Paper Award.
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.
Building a quadruped robot for reinforcement learning research
The goal of this project is to build the open-sourced quadruped robot called Real-Ant. To start with, the 3D models and the codes for the microcontroller as well as serial port communication are available for the robot. So the first task will be to print the components, assemble the robot and test the available codes on the robot. Then in the second stage, we will modify the robot to make it more robust so that it can tolerate mild shocks and can be run for an extended period of time without the need of tightening the screws or replacing the components. For example, we can think of adding soft legs, cushions etc. to protect the belly and the actuators. In addition, we can also simplify the design so that it can be repaired quickly if required.