About this role
We are seeking an Applied Robot Learning Engineer with a strong background in reinforcement learning to deploy learning-based policies for complex manufacturing tasks. This hands-on role involves improving robot performance, addressing sim-to-real challenges, and developing robust policies for manipulation and failure recovery. You will work directly with robotic hardware, focusing on translating RL methods into measurable improvements on physical systems.
What we are looking for 6
- Develop and evaluate VLA models and learning policies on physical robots
- Apply reinforcement learning to enhance manipulation success, efficiency, and robustness
- Implement and evaluate RL algorithms like PPO, SAC, and residual RL
- Design reward functions and success detectors for real-world manipulation
- Address sim-to-real gaps and improve real-world performance
- Deploy learned policies on robotic systems, including bimanual setups