Aaron Hao Tan

I recently finished my PhD at the University of Toronto, where I worked on robot control using RL, diffusion, and multi-modal language models with Dr. Goldie Nejat.

Updated: 12/24

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We introduce a novel Hand-drawn Map Navigation (HAMNav) architecture that leverages pre-trained vision language models for robot navigation across diverse environments, hand-drawing styles, and robot embodiments, even in the presence of map inaccuracies.

We present a novel robot exploration map prediction method called Confidence-Aware Contrastive Conditional Consistency Model (4CNet), to predict (foresee) unknown spatial configurations in unknown unstructured multi- robot environments with irregularly shaped obstacles.

We present MLLM-Search, a novel multimodal language model approach to address the robotic person search problem under event-driven scenarios with incomplete or unavailable user schedules. Our method introduces zero-shot person search using language models for spatial reasoning.

We introduce OLiVia-Nav, an online lifelong vision language architecture for mobile robot social navigation. By leveraging large vision-language models and a novel distillation process called SC-CLIP, OLiVia-Nav efficiently encodes social and environmental contexts, adapting to dynamic human environments.

We present Finder, a novel approach to the multi-object search problem that leverages vision language models to efficiently locate multiple objects in diverse unknown environments. Our method combines semantic mapping with spatio-probabilistic reasoning and adaptive planning, improving object recognition and scene understanding through VLMs.

We propose NavFormer, a novel end-to-end DL architecture consisting of a dual-visual encoder module and a transformer-based navigation network to address for the first time the problem of TDN in unknown and dynamic environments.

The first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments.

We introduce a robotic pillow placement system using a static 6-DOF manipulator, leveraging YOLOv4-tiny, image transformations, and PCA to infer pillow poses and execute macro-actions.

The first extensive investigation of mobile robot inference problems in unknown environments with limited sensor and communication range and propose a new taxonomy to classify the different environment and task inference methods for single- and multi-robot systems.

The development of a novel sim-to-real pipeline for a mobile robot to effectively learn how to navigate real-world 3D rough terrain environments.

A 1:6 scale, multi-wheeled mobile robotic platform with independent suspension, steering and actuation for off-terrain operations.