Thesis Work Tactile Aware Reinforcement Learning for Contact Rich Manipulation

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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 Lead




Your 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.

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