Reinforcement‑learning‑based teleoperation of a humanoid using a motion capture suit

Institut

Lehrstuhl für Mikrotechnik und Medizingerätetechnik

Typ

Semesterarbeit / Masterarbeit /

Inhalt

experimentell /  

Beschreibung

Within our project team at the Chair of Microtechnology and Medical Device Technology (MiMed) we are researching the teleoperation of humanoid robots. As part of this research, a sensor suit has been developed that continuously records and provides kinematic data for the entire human body. Teleoperation allows humans to control remote robots and is essential for collecting real-world motion data. Approaches like the Teleoperated Whole‑Body Imitation System (TWIST), recently published in May 2025 (arXiv:2505.02833) , extends this by combining motion‑capture retargeting and a unified Reinforcemen Learning (RL)+Behavior Cloning (BC) controller to achieve versatile, coordinated whole‑body robot skills including manipulation, locomotion, and expressive motion. A training dataset is constructed by retargeting large-scale MoCap datasets (e.g. AMASS, OMOMO) to humanoid robots, and then a single controller is trained via RL+BC in simulation, which we expect to transfer to our Unitree G1.

 

Thesis goal & methodology

• Develop a motion‑retargeting pipeline using our human motion capture data.
• Train a unified RL+BC whole‑body controller in simulation.
• Deploy the controller on our Unitree G1 robot (excluding finger movements) and evaluate whole‑body capabilities including locomotion and expressive motions.
• Perform comparative analysis metrics: tracking accuracy, robustness, generalization to novel motions, physical locomotion tasks, and latency

Voraussetzungen

Your profile

• Firm understanding and practical experiences in machine learning techniques and reinforcement learning in particular
• At least intermediate Python programming skills
• Experience with ROS2 robotics middleware, simulation and/or humanoids is a plus

 

How to apply

Send an email to julian.ilgtum.de with the following attachments:

• Your CV
• A brief statement of motivation, including relevant background in robotics, RL, control and/or motion capture
• Your preferred start date
• Academic transcripts

Möglicher Beginn

sofort

Kontakt

Julian Ilg, M. Sc.
Raum: MW1129
Tel.: 089-289-15170
Julian.Ilgtum.de