Student Thesis / Research Internship: Robot Learning

04.05.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Topic: Multimodal Robot Learning from Demonstration for Laboratory Automation

Motivation:
Chemical laboratories rely on a range of automated machines for tasks like liquid handling or centrifugation. Yet many repetitive procedures are still performed by human chemists, particularly if dexterous manipulation or adaptation to changing conditions are needed. Fully automating these tasks through traditional programming is impractical, since lab setups or execution protocols can vary significantly over time or between sites.

Learning from demonstration offers a compelling alternative. Instead of explicitly programming every motion and decision, an expert demonstrates a task and the robot learns a generalizable policy from a set of demonstrations. Combined with multimodal sensing, where data from cameras, tactile sensors and the robot's own proprioception are fused to understand the scene and guide execution, this enables a system that can adapt to variations and recover from disturbances.

The approach is not limited to laboratory tasks and can generalize to applications in manufacturing, assembly, etc.


System:
The setup consists of a 4-DOF SCARA robot arm equipped with a parallel gripper, camera(s), tactile sensors and laboratory equipment. A demonstration setup already exists and is capable of showcasing the robot's manipulation capabilities in a laboratory environment.


Research Project:
In this project, you will build on the existing demonstration setup and advance it towards robust learning from demonstration. This includes improving the physical setup, defining meaningful laboratory tasks to be learned and developing a multimodal sensing and learning pipeline. You will work with state-of-the-art models and algorithms in robot learning, adapt/tune them to the SCARA platform and evaluate how reliably new tasks can be acquired from a limited number of expert demonstrations. The work combines elements of robotics, machine learning, sensor fusion, and mechatronics.


Goals:
Improve the existing demonstration setup and define a set of representative laboratory tasks for learning.

Improve and further develop a multimodal sensing pipeline that fuses data from cameras, robot proprioception and (possibly) tactile sensors to inform task execution.

Implement and tune learning-from-demonstration algorithms that allow the robot to acquire new tasks from expert demonstrations.

Evaluate robustness: the learned policies should handle (small) variations in conditions and recover from disturbances during execution.


Requirements:
Interest in robotics, robot learning, sensors and a mechatronics-oriented approach to problem solving.

Good programming skills.

Excited to work on both hardware and software.

Prior experience with any of the following is a plus: ROS, robot learning, manipulation, 3D printing and CAD, deep learning frameworks.


If you are excited about the topic but don't check every box, feel free to reach out anyway!

Kontakt: valdrin.aslani@tum.de