Machine Learning: Whole-Body Control
Develop learned control policies that enable robots to perform dexterous manipulation in unstructured environments.
What you'll do
- Design and train policies for whole-body robotic manipulation
- Develop sim-to-real transfer methods for control policies
- Build and maintain simulation environments for policy training
- Integrate learned controllers with perception and planning
- Evaluate and improve controller robustness in real-world deployments
What we're looking for
- Strong background in robot learning, reinforcement learning, or optimal control
- Experience with sim-to-real transfer for robotic systems
- Proficiency in Python and deep learning frameworks
- Experience with robotic manipulation hardware
- PhD in Robotics, ML, or related field preferred