Key Responsibilities

• Conduct literature reviews to survey state-of-the-art methods in multi-robot task planning and foundation model integration.
• Devise novel solution strategies for task planning in heterogeneous multi-robot teams using LLMs and VLMs.
• Implement proof-of-concept systems and integrate them with simulation or robotic platforms.
• Design and execute evaluation experiments to benchmark performance and generalization.
• Contribute to peer-reviewed publications in leading robotics and AI journals and conferences.
• Collaborate with the research team to share findings, brainstorm ideas, and advance the group’s technical goals.

Minimum Qualifications

• Currently enrolled in a PhD program in Robotics, Computer Science, Electrical Engineering, or a related field.
• Demonstrated experience with Vision-Language Models (e.g., CLIP, BLIP, Flamingo, etc.).
• Solid background in robotics and multi-agent systems.
• Strong programming skills in Python and familiarity with ML libraries (e.g., PyTorch, TensorFlow).
• Excellent communication and documentation skills.

Bonus Qualifications

• Prior experience integrating LLMs (e.g., GPT, PaLM, LLaMA) with perception or planning systems.
• Hands-on experience with ROS, Gazebo, or other robotics simulation frameworks.
• Familiarity with transformer-based architectures for multimodal learning.
• Record of peer-reviewed publications in robotics, AI, or computer vision conferences.