Trainer/Educator: Robotics Data Collection

The Role

The Trainer will conduct hands-on training in robotics data collection, teleoperation, embodied AI workflows, robot sensing and simulation-based data generation for AI model development.

Responsibilities:

• Deliver theoretical and hands-on training on robotics data collection methodologies, embodied AI workflows, and best practices for acquiring high-quality robot learning datasets.
• Train participants on ROS/ROS2, robot sensing technologies, and the integration of sensors such as RGB-D cameras, LiDAR, IMUs, force/torque sensors, and proprioceptive sensors for robotics applications.
• Conduct practical sessions on teleoperation systems, kinesthetic teaching, and demonstration recording, enabling learners to capture robot trajectories and task demonstrations for imitation learning.
• Teach multi-sensor synchronization, data recording, annotation, and documentation of robotics datasets, ensuring adherence to quality standards and industry best practices.
• Deliver hands-on sessions on robotics simulation platforms such as NVIDIA Isaac Sim, MuJoCo, and PyBullet, including synthetic data generation and sim-to-real workflows for embodied AI.
• Mentor and supervise learners in executing end-to-end embodied AI capstone projects, including data collection, dataset validation, documentation, and project presentation.

Qualification:

• M.Tech/MS/Ph.D. in Robotics, AI, Computer Science, Mechatronics or related discipline.
• Experience in robotics research or industrial robotics.
• Hands-on experience with ROS/ROS2.