AI Research Scientist, Perception for Contact Rich Manipulation

Location: Munich, Germany

Department: R&D

Role

As a Senior AI Research Scientist for Perception for Contact Rich Manipulation you will lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. You will explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Your work will involve creating models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. You will collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities that can be deployed on physical hardware for industrial applications.

How your work moves the mission forward

• Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios.
• Design algorithms that enable robots to manipulate complex or deformable objects with high precision.
• Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware.
• Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability.
• Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems.
• Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

Skills you will need to be successful

• PhD in Computer Science, Robotics, or a related field with a focus on machine learning or computer vision.
• 3 years of experience in applied research focused on robotic manipulation or robot learning.
• Proficiency in programming with Python and C++.
• Experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
• Experience developing algorithms for vision-based manipulation or contact-rich interaction.
• Publication record in top-tier robotics or AI conferences (e.g., ICRA, IROS, CVPR, NeurIPS). 

Skills that will differentiate your candidacy

• Experience with reinforcement learning or imitation learning for robotics.
• Familiarity with physics simulators like MuJoCo, Isaac Sim, or Gazebo.
• Experience integrating tactile sensors with visual perception systems.
• Experience in LfD (Learning from Demonstrations), kinesthetic learning.
• Background in sim-to-real transfer techniques for manipulation policies.
• Experience with transformer-based architectures or foundation models in a robotics context.
• Experience deploying machine learning models on edge compute hardwar​e.