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Job Description

This position involves working on a project focused on efficient multimodal robot learning for manipulation, with emphasis on vision-language-action (VLA) systems. The candidate will help in bridging simulation and real robot systems to enable robust, safe manipulation in real environments.

The candidate will:

• Contribute to building manipulation pipelines that combine perception, language, and control.

• Implement and evaluate safety and uncertainty-aware modules to monitor and filter robot behaviors.

• Perform data collection, calibration, and annotation on robotic manipulators and mobile manipulation platforms (such as Mobile ALOHA).

• Develop and maintain simulation environments in Isaac Lab / Isaac Gym / PyBullet / Gazebo for training and testing.

• Work with large manipulation datasets (e.g. LIBERO, RoboCasa, DROID) to guide model training, generalization, and benchmarking.

• Collaborate with the PI and research team to design experiments, analyze results, document findings, and support dissemination (e.g. internal reports, code releases).

Qualifications

• Strong programming skills in Python (experience in C++ is a plus).

• Experience with ROS / ROS2, and robotics simulation tools (e.g. Isaac Lab / Isaac Gym / PyBullet / Gazebo).

• Background in robot manipulation, motion control, and trajectory planning.

• Familiarity with vision-language models / architectures (VLMs/VLAs) or multimodal learning in robotics.

• Experience or strong interest in robot data collection, teleoperation, calibration, and evaluation.

• Exposure to large-scale manipulation datasets such as LIBERO, RoboCasa, DROID, or similar.

• Preferred: experience with Mobile ALOHA or mobile manipulation platforms.

• Good analytical, troubleshooting, and experimental design skills.

• Ability to work independently as well as collaboratively within a research team.