Thesis R&D - #2214896

Who We Are:

We are a rapidly growing embodied AI company revolutionizing human labor. Leveraging cutting-edge robotics and advanced artificial intelligence, we develop transformative technologies that redefine how work is done across multiple industries—empowering businesses to streamline operations, boost productivity, and unlock new possibilities.

Overview:

We are offering a thesis position focused on developing reactive, zero-shot, model-free robotic suction grasping techniques. The research aims to enable robots to generalize to unseen objects without prior training, leveraging closed-loop control and real-time feedback mechanisms.

Research Objectives:

• Develop zero-shot suction grasping policies using multimodal feedback (vision, depth, force).
• Design and evaluate closed-loop control architectures for real-time reactive manipulation.
• Investigate model-free reinforcement learning and visual-language models for generalization to unseen objects.
• Validate methods on industrial-grade robotic systems and contribute to open-source research publications.
  
  

Qualifications:

• Master’s degree in Robotics, Machine Learning, Mechanical Engineering, or a related field.
• Strong background in control systems, reinforcement learning, or robotic manipulation.
• Experience with robot control frameworks (e.g., ROS) and hardware integration.
• Solid programming skills (Python, C++) and familiarity with deep learning frameworks.
• Strong analytical and research skills with a proven publication record being a plus.
  
  

What We Offer:

• Free meals at the workplace
• Flexible working hours
• Option to work from home when needed
• A motivated team and an open corporate culture