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Hudson Kim

Publications and source records attributed to Hudson Kim.

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In-Situ Reconstruction of the International Space Station Using 3D Gaussian Splatting and Astrobee

This article presents a novel 3D reconstruction and mapping of the interior of the International Space Station (ISS) using 3D Gaussian Splatting (3DGS). Using existing grayscale images from the Astrobee free-flying robot dataset, we construct a full 3D splat of the ISS' Kib\=o or Japanese Experiment Module (JEM). 3DGS has in recent years shown promise in providing novel view synthesis of scenes captured from many images or videos, this article applies this approach to human spaceflight systems. We compare our 3DGS architecture to existing methods such as Nerfacto and TensoRF and show that reconstruction improves the state-of-the-art in both scene quality and rendering speed. We show that with as little as 500 in-situ images, a high-fidelity map can be constructed using Astrobee's Navigation Camera (NavCam) during free-flight in the JEM. These reconstructions could enable free-flyers to rapidly create and update interior maps for intra-vehicular habitats like the ISS.

cs.RO

GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks

For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-free policies? We focus on "Variational Automation" (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation. Model-free policies often struggle to close the reliability gap for VA tasks, which must be executed persistently and reliably in commercial and industrial applications. Motivated by prior work on Task and Motion Planning (TAMP) and the Robot Operating System (ROS), we introduce Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs with perception, planning, and control nodes from a Modular Open Robot Skill Library (MORSL). GaP then generates an internal simulation environment to rehearse task instances with different graphs in parallel to iteratively refine the graph structure and parameters to improve success rates and throughput. Evaluation with 8 new open VA task benchmarks, 4 in-simulation and 4 in real-world, suggests that GaP can achieve success rates that significantly outperform baselines. Details, code, and data can be found online: https://graph-robots.github.io/gap

cs.RO