Master's Thesis: Robot Learning from Operational Data

Can robots learn from the work they already do? In this master's thesis at 120ft, you will investigate how operational data can improve robot learning for real assembly tasks. Host: 120ft Factory AB, Stockholm Company supervisor: Sulthan Suresh Fazeela Field: Machine learning and robotics Background 120ft builds Factory OS, a software layer that lets humanoid robots, currently the Unitree G1-D, carry out high-mix, low-volume assembly work. Robot behaviour comes from Vision-Language-Action (VLA) policies trained on recorded demonstrations. Today, almost all training data comes from dedicated teleoperation sessions. Every hour of that data costs an operator's hour. But the robot also generates data every time it runs. The research question Operational rollouts include per-frame tags showing whether a human or a policy was driving. You will investigate whether data from policy-driven episodes can improve the policy, how it can be combined with teleoperation demonstrations, and which data helps rather than hurts. What You Will Investigate This is an offline, experimental project using already-collected rollout data, rather than building a live production pipeline. The goal is to characterise the data and give an evidence-based recommendation on its value for training.

• Compare the operational dataset with curated teleoperation datasets: volume, success and failure balance, and recurring failure modes.
• Test whether fine-tuning on operational data changes policy quality, and whether filtering or weighting by operator-judged outcome labels makes a difference.
• Explore other data-centric questions that emerge, including the reliability and consistency of outcome labels.

What we provide

• A technical supervisor.
• Access to existing operational and curated datasets.
• Access to the training stack for fine-tuning experiments.
• Access to a real Unitree G1-D if additional rollouts are needed.

Who this is for You are a master's student in machine learning or robot learning, comfortable with empirical experimentation and data analysis. Apply Send your CV and a GitHub, LinkedIn or website link. Tell us why this thesis interests you and share an example of work you are proud of.