Master’s Thesis: Human Movement and Activity Models for Production Automation and Embodied AI

05.03.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Human activity is a fundamental part of modern production environments. Real-time dynamic perception and interpretation of human activity patterns helps to optimize operations, increase workspace safety, conserve resources, and support efficient teaming with assistive robots. To that end, production automation hosts a wide array of methods for 3D perception and tracking of full-body poses, gazes and interactions with objects, motion prediction and activity recognition.

Jointly with Occurrence AB we offer several MSc thesis topics concerning human activity in production environments. This is a unique chance to tackle industrial-grade use-cases of production automation, work with real-world data and gain experience with deployed software and data frameworks.

Possible topics include:

• Accurate perception of human poses from multiple heterogeneous vision sources in the environment
• Open-vocabulary activity recognition using vision-language models
• Status detection (e.g. engagement or distraction) and dynamic task-oriented gestures interpretation
• Ground truth gaze data collection to improve the camera-based gaze estimation
• Training on large amounts of human movement data
• Mobile multimodal camera-lidar rig for ground truth 3D environment perception

Prerequisites:

• Strong machine learning and deep learning background
• Excellent programming skills in Python
• Preferable experience in PyTorch or TensorFlow
• Background in Informatics or Robotics (CIT School)
• Independent and self-motivated working style

If you are interested, just send an email to andrey.rudenko@tum.de and karen.biehl@occurrence.se with a short CV and your grade report.

Kontakt: andrey.rudenko@tum.de

Mehr Information

https://www.ce.cit.tum.de/pins/open-positions/student-positions/