Robotics Expertise
Comprehensive Robotics Foundation
My expertise in robotics is built upon numerous academic courses and hands-on application with manipulators and humanoid robots. From core principles to joint implementation with AI.
Especially the joint integration of robotics with AI is what really excites me. This is also why I chose to write my master thesis and my first published paper in this direction.
Explore my work on [Variable Stiffness for Robust Locomotion through Reinforcement Learning] published at IFAC.
Core Skills & Services
With a multidisciplinary background in electrical engineering, control systems, and artificial intelligence, I bring a full-stack approach to modern robotics. My work spans academic research, hands-on prototyping, and intelligent system design. Here's how I can contribute:
- Robotics Systems Development: Full-cycle development of robotic platforms (locomotion & manipulation), ROS (Robot Operating System) expertise (Kinetic & Noetic)
- AI-Driven Control & Learning: Implementation of Supervised/unsupervised techniques and Reinforcement learning.
- Cross-Disciplinary Thinking: My strong background in electrical engineering, mechanical engineering provides me with a solid foundation. I am comfortable moving between code, circuits, and real-world deployment
Motivation & Experience
My journey into robotics has been driven by a curiosity and a hands-on mindset. With a solid academic foundation in areas kinematics, dynamics, interaction control, and the linear algebra that supports them, I've been able to turn complex theory into practical, working systems. I've had the opportunity to implement a variable stiffness locomotion policy on the GO2 humanoid robot, and to assemble and integrate control systems for a 6DOF robotic arm. These experiences have shaped my approach to robotics—not just as a technical field, but as a space where creative problem-solving and real-world application intersect. I really enjoy this field since multiple disciplines (like electrical, mechanical and software engineering) come together to create something that is more than the sum of its parts. Since I have background in electrical engineering, I deeply value the hardware side of robotics. With my experience in both manipulation, locomotion, applied ai and a cross-functional understanding I am able to see systems as a whole and design solutions that are both innovative and grounded.
AI & Robotics
Broadly speaking, there are two domains in robotics:
- classical control domain:which heavily relies on mathematical modeling and control theory and optimization based techniques. In which I have a solid foundation.
- AI domain:which is more data driven and relies on machine learning techniques.
Project on Locomotion
Within my master thesis I worked on the integration of a variable stiffness locomotion policy on the GO2 humanoid robot. This work was published at IFAC and is available for download here. This approach applies reinforcement learning to train an ai agent to walk. Additionally to the previous approaches which learn to perdict the joint positions, we trained the agent to learn the joint stiffnesses. Normally, the stiffness of the joints is tuned manually, which can be time consuming and not optimal. The approach aims to automate the adjustment of motor stiffness according to task requirements, eliminating the need for manual tuning. Key Results
- Velocity tracking:Our method enabled more accurte velocity tracking than position based approaches (baselines).
- Push recovery:Our method outperformed the baselines showing robust learned gait.
- Energy efficiency:While showing competetive results with low stiffness policies it outperforms high stiffness policies.
Project in Manipulation
During my employment at the Institute of Automation and Control at TU Vienna, I was involved in a project, under the guidance of Shail Jadav, that focused on the development of a 6DOF robotic arm. This project is build on the open manipulator platform, but instead of featuring just 4 DOF we added 2 additional DOF to the arm, enabling the robot to be as verasatile and flexible as possible. The arm is equipped with a gripper and a camera, allowing it to perform a variety of tasks. The control is system is based on ROS kinetic/noetic.
Goal
The overal goal of the project is to solve the tower of hanoi problem with the arm, autonomously. Therefore, subgoals have to be reached:
- Kinestetic Teaching:Physically guiding the robot to record pick and place movements. This trajectories should be storable and replayable.
- Learning from demonstrationsOne recorded trajectory might solve one problem. But what if I want to pick an object from a different location? This is where Dynamic Movement Primitives comes in. We use DMPs to generalise the recorded trajectories to pick/place from to any location.
- Object detection and recognitionUsing the camera the objects must be detected, segemented and locate the object in the world frame, so the robot is able to grasp it.
- Reasoning with LLMsOnce the basic motions and the location is determined all is left is the reasoning to solve the tower of hanoi problem. An LLM, based on the outputs of the other modules and the present state, is used to determine the next steps.
The exciting part within this project is the interdisciplinarity of the project. We have manipulation, computer vision and reasoning with LLMs. Furthermore, the cost effective robotics platform despite looking simple is very similar to more advanced robots like the KUKA LBR iiwa. Moreover, the developed concepts are build on the ROS middleware, which is widely used in the robotics community. This means that the concepts can be transfered to other robotic platforms as well.
This project is still ongoing and I am looking forward to the results ...