Reinforcement Learning & Controls Research Scientist- Spot Behavior
- Design and deploy RL systems that improve Spot's mobility and robustness across challenging terrain.
- Tune and validate low-level controllers (e.g., PD/PID, whole-body control) at the interface with learned policies.
- Build and maintain simulation environments (e.g., Isaac Sim, MuJoCo) to train and validate policies before hardware deployment.
- Analyze robot data logs to diagnose control failures and iterate on controller design.
- Test and debug directly on our in-house Spot fleet, taking a first-principles approach to failure analysis.
- Write production-ready code in Python and C++.
- MS or PhD in Robotics, Mechanical Engineering, Computer Science, or related field.
- 3–6 years of experience deploying RL or learning-based control policies on physical hardware.
- Strong foundations in classical control theory, including stability analysis, state estimation, and low-level actuation.
- Experience with real-time software constraints and control loop design.
- Proficiency in Python and C++.
- Familiarity with modern deep RL frameworks (e.g., PyTorch, RLlib).
- Experience with legged robotics or contact-rich locomotion systems.
- PhD in a relevant field.
- Familiarity with whole-body control or model predictive control (MPC) for legged systems.
- Experience with state estimators (e.g., EKF, contact estimation) in robotics.
- Experience with sim-to-real transfer and domain randomization.
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