Robotics Researcher – Manipulation

We're building the systems that let an advanced humanoid robotics platform pick up a box, open a drawer and operate tools. As a Robotics Researcher in Manipulation, you'll develop the grasp planning, contact‑rich control and learned task policies that power the robot's hands. You'll work across model‑based control, imitation learning and reinforcement learning, with the bar set by whether it works on the physical robot in a real environment, not just in simulation.

This role combines research depth with a relentless focus on shipping to hardware.

• Build grasp planning and contact-rich control pipelines that generalise across varied objects and environments
• Design and run data collection and teleoperation infrastructure to feed policy training at scale
• Train manipulation policies using imitation learning, reinforcement learning or hybrid approaches, iterating until they work in the real world
• Integrate manipulation with the perception stack and broader autonomy pipeline
• Systematically diagnose hardware failure modes and drive improvement
• Contribute to open‑source manipulation research and tooling

• Strong foundations in robotics, control theory and motion planning
• Hands‑on experience building and deploying manipulation systems on real robotic platforms
• Strong Python and C++; experience with PyTorch or JAX
• A track record of taking manipulation research from prototype to hardware deployment
• Experience with data collection infrastructure and teleoperation for policy training

• Diffusion policies, transformer‑based policy architectures, or large‑scale foundation models for manipulation
• Prior work on dexterous or in‑hand manipulation
• Publications at RSS, ICRA, CoRL or equivalent