Robotics Research Internship-Locomotion & Planning Fall 2026 - Field Ai

Field AI is building the future of autonomy—from rugged terrain to real-world deployment. We’re on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments.

We are offering a Summer 2026 internship focused on learning-based locomotion and planning for PhD students interested in advancing autonomous legged robot capabilities. As a research intern, you will work at the intersection of reinforcement learning, locomotion control, and learned planning, developing integrated systems that enable robots to move and navigate intelligently through complex, unstructured environments.

You will collaborate closely with Field AI research scientists and engineers to design experiments, develop locomotion and planning systems, and validate ideas in simulation and on real hardware. This internship emphasizes building tightly integrated learning-based systems that connect low-level locomotion with high-level planning, translating research into practical, deployable capabilities for real-world robotics.

What You’ll Get To Do

Advance RL-Based Locomotion and Learned Planning Research

• Design, implement, and evaluate reinforcement learning pipelines that tightly integrate locomotion control with learning-based planning.
• Explore how learned planners can inform and adapt locomotion behaviors across varied terrain and dynamic conditions.
• Contribute to research projects from early-stage ideas through simulation experiments and on-robot validation.

Bridge Locomotion and Planning Across the Sim-to-Real Gap

• Develop and refine sim-to-real transfer strategies, including domain randomization, system identification, and adaptive methods, for integrated locomotion-planning systems.
• Build and leverage GPU-accelerated simulation environments (Isaac Gym, Isaac Lab, MuJoCo) for scalable training and evaluation.
• Test and iterate on policies using real legged robot platforms in unstructured environments.

Build Systems That Connect Research to Deployment

• Translate research concepts into working robotic systems tested on real hardware.
• Develop experimental setups and tooling to support data collection, evaluation, and reproducibility.
• Help ensure locomotion and planning systems are robust, field-relevant, and ready for iterative improvement.

Collaborate Across the Full Robotics Stack

• Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
• Incorporate real-world telemetry and field data to refine models and improve generalization.
• Engage with researchers and engineers across the team to align experiments with broader autonomy goals.

Rapidly Iterate and Learn