ABBThesis Work - Tactile-Aware Reinforcement Learning for Contact-Rich Manipulation
AI in this role
Conduct thesis research on tactile-aware reinforcement learning for contact-rich robotic manipulation in simulation.
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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.
This Position reports to:
R&D Center LeadYour role and responsibilities
This thesis combines both investigation and prototype development in the area of tactile-guided robotic manipulation. The student will investigate how tactile sensing can improve the performance and robustness of contact-rich manipulation tasks. A digital twin of a robot arm equipped with a Psyonic/Linkerbot Hand will be provided by the supervisors in NVIDIA Isaac Sim. Building on this foundation, the student will extend the simulation framework with tactile sensing capabilities, develop reinforcement learning training environments and pipelines in NVIDIA Isaac Lab, and evaluate tactile-guided manipulation policies in simulation, with optional validation on real hardware.
The work is expected to include:
- Conducting a literature review on tactile sensing, dexterous manipulation, and robot learning methods on Isaac Sim & Isaac Lab.
- Developing tactile sensing simulation based on contact information available in Isaac Sim.
- Implementing and training reinforcement learning policies for contact-rich manipulation tasks such as grasp stabilization, object manipulation, pick-and-place, and insertion.
- Comparing tactile-aware policies against proprioception-only baselines.
- Evaluating policy performance in terms of task success rate, robustness, and sim-to-real transfer capability.
The project has a well-defined technical direction, but students are encouraged to propose and explore their own ideas regarding tactile representations, learning algorithms, task design, or sim-to-real transfer strategies. We are open to suggestions that align with the overall goal of improving tactile-guided robotic manipulation.
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
The thesis will be jointly supervised by two supervisors with complementary expertise in robotics and AI. Students will receive guidance on simulation development and tactile sensing, as well as on robot learning methods such as reinforcement learning and imitation learning. Additional expertise in robotics and control systems will be available throughout the project, providing comprehensive support across both AI and robotics domains.
Qualifications for the role
- Master students interested in robotics and reinforcement learning
- Python, C++, ROS 2 experience is preferred
- Robotics fundamentals (kinematics, dynamics, control)
- Machine learning basics
- Linux and Git
More about us
Recruiting Manager LiWei Qi, +46 73 021 2309, Supervisors: Tong Hui, tong.hui@se.abb.com Zhen Li zhen.li@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.
We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.
How we rate this
Thesis Work - Tactile-Aware Reinforcement Learning for Contact-Rich Manipulation at ABB rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where robotics was part of your work. What did you do?
- Tell me about a project where reinforcement learning was part of your work. What did you do?
- Tell me about a project where simulation was part of your work. What did you do?
- Tell me about a project where tactile sensing was part of your work. What did you do?
- Tell me about a project where tactile aware manipulation was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Robotics, Reinforcement Learning, Simulation, Tactile Sensing, and Tactile Aware Manipulation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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