Thesis Work - Whole-Body Teleoperation and Imitation Learning for Humanoid Robots

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This role sits within ABB's Robotics business, a leading global robotics company. We're entering an exciting new chapter as we’ve announced the plan for SoftBank Group to acquire ABB Robotics. SoftBank is a globally recognized technology group and investor/operator focused on AI, robotics, and next-generation computing.  By joining us now, you’ll be part of a pioneering team shaping the future of robotics—working alongside world-class experts in a fast-moving, innovation-driven environment.

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Your role and responsibilities

Humanoid robots are increasingly seen as versatile platforms for real-world automation, able to operate in environments built for humans. Yet, teaching them complex, coordinated tasks — combining locomotion (walking, balancing) and manipulation (grasping, pushing) — remains an open research challenge.

This thesis addresses that challenge through two connected goals: first, building a whole-body teleoperation system for the Unitree G1 humanoid robot, where a human operator drives the robot using an AR (Augmented Reality) headset (providing head pose and hand tracking) and wearable inertial sensors on the legs; second, using the motion data collected through teleoperation to train Imitation Learning (IL) policies — algorithms that allow the robot to autonomously reproduce the demonstrated behaviors, without human intervention.

Details:

• Period: January to July 2027
• Number of credits:  30 ECTS/högskolepoäng (hp)
• Number of students for this thesis work: 1
• Location: on-site, Västerås

This is a research and prototyping project with a clear end-to-end pipeline in mind, but we are open to suggestions on specific methods or tools. The main tasks are:

• Literature review: review the state of the art on whole-body humanoid teleoperation, motion retargeting, and Imitation Learning for robotics. Identify relevant methods, benchmarks, and open challenges to inform the design choices of the project.
• Teleoperation setup: Integrate an AR headset (e.g., Meta Quest) and leg-worn inertial measurement units (IMUs) into a real-time system that tracks the operator's full-body pose and maps it to the Unitree G1's joints.
• Data collection: Use the teleoperation system to record demonstrations of whole-body tasks (e.g., walking while reaching, picking and placing objects).
• Policy training: Train selected Imitation Learning models on the collected data.
• Validation: Test the full pipeline first in simulation (e.g., Isaac Sim), then on the real robot.

 Qualifications for the role

• Enrolled in a Master's program in Robotics, Computer Science, Electrical Engineering, Mechatronics or equivalent
• Solid programming skills in Python and/or C++
• Basic knowledge of robot kinematics and control
• Experience with Imitation Learning, Reinforcement Learning and deep learning frameworks (e.g., PyTorch) is a plus
• Experience with physics simulators (e.g., MuJoCo, Isaac Sim) is a plus
• Hands-on experience with real robot systems is a plus
• Fluent in English (written and spoken)

More about us

Recruiting Manager LiWei Qi, +46 73 021 2309, Supervisor: Marco Iannotta, marco.iannotta@se.abb.com will answer your questions.

Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English.

We look forward to receiving your application!

A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.

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