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About

Bhoram Lee is a robotics researcher whose work focuses on advancing autonomous systems through innovative approaches to environmental representation, motion planning, and human-robot interaction. His primary research areas include continuous mapping, sampling-based motion planning, and manipulation for robotic systems operating in complex, unstructured environments. Lee's most impactful contribution is his work on "Online Continuous Mapping using Gaussian Process Implicit Surfaces" (2019, 50 citations), which introduced a method for robots to efficiently represent environments using sparse sensor data—a significant improvement over traditional grid-based approaches. He also played a key role in Team THOR's entry in the DARPA Robotics Challenge Finals (2015), where his team tackled human-in-the-loop disaster response under challenging conditions. Lee has further advanced adaptive motion planning with high-dimensional Gaussian Mixture Models (2017, 13 citations) and developed constrained sampling-based methods for grasping and manipulation (2018, 13 citations). His recent work on HIO-SDF (2024) introduces hierarchical incremental online signed distance fields for large-scale robotic mapping. With a strong focus on bridging simulation and real-world testing, Lee's research continues to push the boundaries of safe, efficient, and intelligent robotic systems.