Technical Director, Center for Vision Technologies, Vision and Robotics Laboratory
Han-Pang Chiu, Ph.D., Technical Director of the Scene Understanding and Navigation (SUN) Group at SRI International, leads government and commercial projects to develop innovative solutions for real-world applications to multi-sensor navigation, multi-modal target detection/classification, robotic autonomy, and mobile augmented reality.
Han-Pang has been chief scientist and technical lead in many DARPA, ONR, and US Army research programs. He has been a pioneer in factor graph formulation for multi-sensor navigation. The paper “Stable Vision-Aided Navigation for Large-Area Augmented Reality” co-authored and presented by him received the best paper award in the IEEE Virtual Reality 2011 conference. His work also supports a few spin-off companies from SRI. Recently, he has also been leading SRI’s efforts in semantic navigation, which develops new deep learning techniques to derive and utilize high-level semantic scene information for robotic autonomy.
Prior to joining SRI, Han-Pang was a postdoctoral researcher in Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT), where he worked on a DARPA-funded project to develop computer vision techniques for robot grasping. He received his Ph.D. in Computer Science from MIT in 2009, and holds an MBA degree in management information systems from National Taiwan University.
Recent publications
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Graph2Nav: 3D Object-Relation Graph Generation to Robot Navigation
We propose Graph2Nav, a real-time 3D object-relation graph generation framework, for autonomous navigation in the real world.
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Enabling Scalable Mineral Exploration: Self-Supervision and Explainability
Abstract Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) is important for automating and accelerating the workflow to critical mineral assessment. Recent MPM works have explored Deep Learning (DL) as…
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GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping
Abstract Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a challenging problem as it requires the analysis of associations between large-scale multi-modal geospatial data and a few historical mineral…
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SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments.
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Machine Learning Aided GPS-Denied Navigation Using Uncertainty Estimation through Deep Neural Networks
We describe and demonstrate a novel approach for generating accurate and interpretable uncertainty estimation for outputs from a DNN in real time.
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Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement
We propose an auxiliary pseudo-label refinement network (PRN) for online refining of the pseudo labels and also localizing the pixels whose predicted labels are likely to be noisy.