This project focuses on long-horizon embodied navigation for heterogeneous multi-robot collaboration in previously unseen, partially observed, and open-ended environments, where different types of embodied agents must efficiently locate target by sharing observations, spatial memory, and navigation plans. Guided by multimodal goal inputs, agents are expected to ground goals, perceive their surroundings, leverage spatial memory, reason about spatial relationships, and identify the correct target during navigation. Throughout this framework, we assume agents can continuously update their representation of the environment through online observations and collaborate with other agents that may have different sensing, mobility, or task capabilities. These tasks will be approached using vision-language models and multimodal foundation models, enabling agents to perform open-vocabulary perception, multimodal goal grounding, spatial reasoning, and high-level planning within a deployable embodied navigation framework.
• Possess a master’s degree in Computer Engineering, Robotics, or a closely related discipline.
• Strong coding skills in Python and experience with deep learning frameworks such as PyTorch.
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We are seeking a Research Fellow to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large language models (LLMs), vision-language models (VLMs), and vision-language-action models (VLAs), across a range of model scales and application scenarios. The Research Fellow will advance efficient AI techniques that enable scalable deployment on cloud, edge, and robotic platforms. The Research Fellow will contribute to the University’s mission by conducting high-impact research, developing innovative efficiency-oriented methods, and disseminating research outcomes through publications, collaborations, and open-source contributions
Key Responsibilities:
Develop accelerated AI/ML and robotics algorithms that significantly reduce computation cost, memory footprint, and power consumption.
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