Fully Funded PhD Position: Robot Skill Learning for Long-Horizon Assembly Tasks

The Industrial AI Group at The University of Auckland is seeking exceptional candidates for a fully funded PhD position focused on developing novel approaches to robot skill learning for complex assembly tasks.

Project Description

Modern manufacturing processes often involve intricate assembly sequences requiring precise manipulation and complex contact interactions. While robots excel at repetitive tasks in structured environments, they still struggle with adaptable, contact-rich manipulation required for sophisticated assembly operations. This project aims to advance the state-of-the-art in robot learning for long-horizon assembly tasks by developing new algorithms that can effectively:

• Learn from human demonstrations while capturing subtle contact dynamics
• Decompose complex assembly sequences into reusable primitive skills
• Generate robust policies that can handle variations in parts and environmental conditions
• Scale to long-horizon tasks through hierarchical learning approaches
• Bridge the sim-to-real gap for contact-rich manipulation

Funding Details

• Full tuition coverage for the duration of 36 months
• Monthly living allowance for the duration of 36 months
• Access to state-of-the-art robotics hardware and computing facilities

Requirements

Essential

- Master's degree in Robotics, Computer Science, Mechanical Engineering, or related field

- Strong programming skills (Python, C++)

- Solid foundation in machine learning and robot control

- Excellent academic record with relevant research experience

- Have published at top robotics conferences or journals

- Strong written and verbal communication skills in English

- Strong motivation to conduct excellent research

- Excellent interpersonal skills for developing and managing relationships

Desired

- Experience with deep learning frameworks (PyTorch, TensorFlow)

- Background in reinforcement learning or imitation learning

- Hands-on experience with robotic systems

- Experience with physics simulation environments (MuJoCo, IsaacGym)

Research Environment

You will join a dynamic research group working at the intersection of robotics, machine learning and industry automation. Our lab is equipped with multiple robotic arms, advanced sensing systems, and high-performance computing infrastructure. You will collaborate with leading researchers in the field and have opportunities to engage with industrial partners.

Supervisors

• Main supervisor: Dr. Yuqian Lu
• Co-supervisor: Prof. Bruce MacDonald

Application Process

Please submit the following documents:

1. Detailed CV including academic background and research experience
2. Research statement (max 2 pages) outlining your interests and their alignment with this position
3. Academic transcripts
4. Contact details of two references
5. Relevant publications or technical reports

Applications should be submitted to [Email Address Removed] with the subject line "PhD Application - Robot Assembly Learning".

Important Dates

- Application Deadline: 1^st March 2025

- Expected Start Date: 1 July 2025 or asap

 Contact

For informal inquiries about the position, please contact:

Dr. Yuqian Lu

Email: [Email Address Removed]