Motion Control and Planning Intern

As a Reinforcement Learning Intern, you will be at the forefront of making humanoid robotics a reality. This is a hands-on role where you will work directly with a senior mentor to plan, develop, and execute a high-impact project that showcases new functionality on our physical robot systems. You will bridge the gap between simulation and reality, gaining deep technical exposure to hardware, software, and project management in a fast-paced, collaborative environment.

• Project Ownership: Partner with a mentor to define and execute a scoped RL project—from initial simulation to deploying a functional demo on humanoid hardware.
• Sim-to-Real Development: Iterate on learning algorithms in high-fidelity simulators and assist in the "sim-to-real" transfer process to ensure robust performance on physical robots.
• Collaborative Engineering: Work alongside our robotics and hardware teams to troubleshoot system-level challenges and understand the interplay between code and motors.
• Pipeline Optimization: Help refine training pipelines or data processing tools (such as motion retargeting from human demonstrations) to improve how our robots learn.
• Technical Communication: Present your project findings and hardware results to the broader engineering team, gaining experience in how to translate data into technical milestones.

• Foundational Knowledge: A strong theoretical understanding of Reinforcement Learning (RL) and robot dynamics.
• Technical Stack: Proficiency in Python and experience with common RL frameworks (e.g., PyTorch, JAX).
• Simulation Tools: Familiarity with physics simulators such as MuJoCo, IsaacGym, or Drake.
• Coding Standards: Ability to write clean, maintainable code; exposure to C++ is a significant plus.
• Problem-Solving Mindset: A "hacker" mentality—you are excited to get your hands dirty, troubleshoot hardware glitches, and see your code move a physical system.
• Collaboration: Excellent communication skills and a desire to learn from a world-class team of engineers.

• Currently enrolled in a BS, MS, or PhD program in Robotics, Computer Science, Mechanical Engineering, or a related technical field.
• Prior experience (academic or personal projects) involving robotic control or machine learning.
• Experience with legged robots or robotic manipulators is a plus but not required.

• Prolonged periods of sitting at a desk and working on a computer
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate