6 papers
X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Haitong Wang, Aaron Hao Tan, Angus Fung +1
Existing navigation methods are primarily designed for specific robot embodiments, limiting their generalizability across diverse robot platforms. In this paper, we introduce X-Nav…
Find Everything: A General Vision Language Model Approach to Multi-Object Search
Daniel Choi, Angus Fung, Haitong Wang +1
The Multi-Object Search (MOS) problem involves navigating to a sequence of locations to maximize the likelihood of finding target objects while minimizing travel costs. In this pap…
Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach
Aaron Hao Tan, Angus Fung, Haitong Wang +1
Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such a…
4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration
Aaron Hao Tan, Siddarth Narasimhan, Goldie Nejat
Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face energy and communication challenges which directly affect their ability to explore thes…
OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation
Siddarth Narasimhan, Aaron Hao Tan, Daniel Choi +1
Service robots in human-centered environments such as hospitals, office buildings, and long-term care homes need to navigate while adhering to social norms to ensure the safety and…
MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal Large Language Models
Angus Fung, Aaron Hao Tan, Haitong Wang +2
Robotic search of people in human-centered environments, including healthcare settings, is challenging as autonomous robots need to locate people without complete or any prior know…