Emergent Systems in Production

Tue, 09 Jun 2026 00:00:00 +0000

Project Description

This 3-semester M-APR project (starting WiSe 2026/2027) compares centralised and decentralised coordination strategies for heterogeneous robot fleets in simulated and real production scenarios. The scientific contribution lies in the empirical characterisation of the transition between both coordination paradigms under varied adversarial conditions.

Structure over Three Semesters

Semester 1 — Foundations and Simulation

Literature review on decentralised coordination and multi-agent reinforcement learning
Building the ROS 2-based simulation environment (at least two agent types: mobile and stationary)
Implementation of a centralised baseline and a simple decentralised approach

Semester 2 — Comparative Study and Learning-Based Coordination

Implementation of learning-based decentralised coordination (MARL)
Systematic comparison under varied adversarial conditions (communication failure, machine failure, dynamic order changes)
Statistical evaluation and identification of transition points between coordination regimes

Semester 3 — Validation and Publication

Transfer of selected scenarios to real hardware at TTZ
Evaluation of sim-to-real transferability
Completion of master’s thesis and preparation of a conference publication (MAPR conference)

Requirements

Bachelor’s degree in electrical engineering, computer science, mechatronics, robotics or equivalent
Programming in C++ and/or Python
Basic knowledge of machine learning / reinforcement learning
Experience with ROS 2 and simulation (Gazebo or similar) is an advantage
Interest in multi-agent systems and production robotics

This topic can also be completed as a project or master’s thesis subject to agreement.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de
Co-Supervisor	Waldemar Haag	waldemar.haag@th-nuernberg.de

Location: TTZ Nürnberger Land, Lauf an der Pegnitz

Further information: TTZ Nürnberger Land · Mobile Robotics Lab

2R Robot Arm Lab Experiment – Gravity Compensation and Reinforcement Learning

Mon, 08 Jun 2026 00:00:00 +0000

Motivation

The “Intelligent Robotics” practical course in the M-IAS master’s programme currently lacks a hands-on experiment bridging classical model-based control and modern learning-based methods. A 2R robot arm with torque control offers exactly this: students first experience the physical effects of gravity on the real system, then learn how an RL agent solves the same task in a data-driven way.

Work Packages

Hardware

Selection and commissioning of BLDC motors and motor drivers (e.g. ODrive / VESC)
Mechanical design and fabrication of arm segments (CAD, 3D printing or aluminium profile)
Integration of encoders and wiring

Software & Control

Implementation of forward and inverse kinematics
Model-based gravity compensation controller (real-time torque compensation)
ROS integration: topics, services and ros2_control

Reinforcement Learning

Building a simulation environment (Gazebo or MuJoCo) for RL training
Training an agent (e.g. PPO / SAC) on a target-reaching task
Sim-to-real transfer and evaluation on the real arm

Documentation

Creating lab instructions for use in the M-IAS practical course

Requirements

Knowledge of control theory and robotics (kinematics, dynamics)
Programming skills in Python and/or C++
Ideally experience with ROS
Interest in machine learning and reinforcement learning
Enthusiasm for hands-on hardware work and prototyping

This topic can be completed as a project or master’s thesis subject to agreement.

Supervision

Role	Name	E-Mail
Supervisor	Prof. Dr. Christian Pfitzner	christian.pfitzner@th-nuernberg.de

Reinforcement Learning | Mobile Robotics Research Group — Prof. Dr. Christian Pfitzner

Emergent Systems in Production

Project Description

Structure over Three Semesters

Requirements

Supervision

2R Robot Arm Lab Experiment – Gravity Compensation and Reinforcement Learning

Motivation

Work Packages

Requirements

Supervision