Thesis Work for generating photo realistic virtual training data for physical AI models

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Software Engineering · Robotics & Automation

In short

This thesis work focuses on generating photo-realistic virtual training data for physical AI models in robotics. It involves creating 3D digital twins, researching simulation techniques, and evaluating AI performance using methods like ACT. The goal is to find a balance between simulation fidelity, speed, and complexity.

Responsibilities

Research solutions for simulating robots and capturing video footage en masse.
Create 3D digital twins of robot setups using techniques like Gaussian Splatting and mesh generation.
Evaluate robot performance using physical AI methods (e.g., ACT) trained on virtually generated video reference.
Explore the necessary level of simulation fidelity for training AI models, balancing speed, complexity, and performance.

Requirements

Engineering student with a background in robotics, computer science, or a similar program.
Experience with machine learning and AI training, preferably in AI vision systems or imitation learning.
Knowledge of linear algebra.

Desired Qualifications

Experience with 3D rendering and CAD and/or 3D modeling techniques is a plus.
Hands-on experience with ABB industrial robots is a plus.

Benefits

Partly on-site work.
Thesis period: January - June 2027.
30 ECTS/högskolepoäng (hp) credits.
Opportunity to work with world-class experts in a fast-moving, innovation-driven environment.
Potential for future career opportunities with ABB.

Skills

AI vision systemsimitation learningmachine learninglinear algebra3D renderingCAD3D modellingGaussian Splattingmesh generationTransformers

#AI#Robotics#Machine Learning#Computer Science#3D Rendering#CAD#Thesis

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