Robotics Research Internship-Locomotion & Planning (Summer 2026)

What You’ll Get To Do

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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
 
• Prototype quickly, run experiments in simulation and on hardware, and analyze results rigorously.
• Balance exploratory research with concrete deliverables over the course of the internship.
• Debug system-level issues spanning simulation, software, hardware, and learning.

What You Have

• Current PhD student in Robotics, Computer Science, Mechanical Engineering, AI/ML, or a closely related field.
• Research experience in reinforcement learning for continuous control, locomotion, or learning-based planning.
• Strong foundation in contact dynamics, control theory, and kinematics.
• Proficiency in Python and/or C++, with experience using robotics or ML tooling.
• Familiarity with physics-based simulators such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet.
• Experience designing experiments and evaluating results on robotic systems (simulation or hardware).
• Curiosity, initiative, and a strong interest in building autonomous systems that operate in the real world.

The Extras That Set You Apart

• Hands-on experience with legged robot platforms (quadrupeds, wheeled-quadrupeds, bipedal systems, or exoskeletons).
• Experience with sim-to-real transfer for locomotion or planning policies.
• Background in learning-based planning, motion planning, or terrain-adaptive control.
• Familiarity with ROS or ROS2.
• Publications, preprints, or open-source contributions in locomotion, RL, planning, or control.
• Experience deploying neural network controllers on resource-constrained or real-time robotic platforms.
• Interest in bridging cutting-edge research with practical, field-ready robotic systems.