Research Assistant — Robotic Manipulation Simulation & Sim-to-Real Transfer

Responsibilities

• Design, build, and validate high-fidelity robotic manipulation simulations in NVIDIA Isaac Sim / Isaac Lab, including scene construction, contact and physics tuning, and sensor modeling.
• Develop, train, and benchmark manipulation policies (model-based control and/or reinforcement and imitation learning) for dexterous hands and manipulators.
• Design and execute sim-to-real transfer pipelines, including domain randomization, system identification, and calibration to reduce the sim-to-real gap.
• Deploy simulated policies onto physical robot hardware, quantify sim-to-real performance gaps, and iterate on both simulation and hardware.
• Integrate perception, tactile/force sensing, and control stacks so that they behave consistently across simulation and hardware.
• Maintain reproducible codebases, data-acquisition workflows, and technical documentation.
• Contribute to design reviews, technical reports, and progress updates for sponsor deliverables, and help mentor junior graduate and undergraduate students.
• Collaborate with faculty, PhD students, and external industry and university partners on shared research goals.

Requirements

• PhD in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related field (earned, or with degree requirements substantially complete).
• Demonstrated research experience in robotic manipulation, reinforcement/imitation learning, or model-based control.
• Hands-on experience with physics-based robot simulation, especially NVIDIA Isaac Sim / Isaac Lab (MuJoCo, PyBullet, or comparable also considered).
• Proficiency in Python, with familiarity in PyTorch or a comparable machine-learning framework.
• Experience with sim-to-real transfer techniques such as domain randomization, system identification, or calibration.
• Experience deploying and validating algorithms on physical robot hardware.
• Strong record of research output (e.g., peer-reviewed publications) and clear written and verbal communication.

Qualifications

• PhD in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related field (earned, or with degree requirements substantially complete).
• Demonstrated research experience in robotic manipulation, reinforcement/imitation learning, or model-based control.
• Hands-on experience with physics-based robot simulation, especially NVIDIA Isaac Sim / Isaac Lab (MuJoCo, PyBullet, or comparable also considered).
• Proficiency in Python, with familiarity in PyTorch or a comparable machine-learning framework.
• Experience with sim-to-real transfer techniques such as domain randomization, system identification, or calibration.
• Experience deploying and validating algorithms on physical robot hardware.
• Strong record of research output (e.g., peer-reviewed publications) and clear written and verbal communication.

Skills

• Experience with ROS / ROS 2 and real-time robot control.
• Familiarity with dexterous robotic hands (e.g., LEAP hand variants) and tactile, force/torque, or vision-based sensing.
• GPU-accelerated, large-scale parallel simulation and training (Isaac Lab / Orbit).
• USD / OpenUSD asset authoring for simulation environments.
• Experience mentoring students and leading collaborative research efforts.

Pay

Department Mechanical Engineering Operations Compensation Range: USD $22 – $28 per hour.