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.

What you'll do

Research, develop and deploy manipulation policies for dexterous task execution

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

What you'll bring

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

Nice to have

Diffusion policies, transformer-based policy architectures, or large-scale foundation models for manipulation

Prior work on dexterous or in-hand manipulation

Contact-rich or deformable object manipulation

Publications at RSS, ICRA, CoRL or equivalent