arXiv subjects

Long Yin Chung

Publications and source records attributed to Long Yin Chung.

4 recordsLinked to original sources

arXiv2026

Fiber Bragg Grating Whiskers for Bioinspired Hydrodynamic Perception on Underwater Robots

Harbor seals track hydrodynamic trails with their vibrissae, enabling passive perception of moving targets in dark or turbid water. Inspired by this capability, we present compact fiber Bragg grating (FBG) whiskers for underwater robots. Like seal whiskers, they have a non-uniform taper and elliptical cross-section. Controlled towing experiments show a monotonic relative-flow response from 0.1 to 0.6 m/s, a strong reduction of self-induced oscillation relative to a cylindrical baseline, and a pronounced dependence on angle of attack. Experiments with a pitching foil show that the whiskers can detect the characteristic vortices shed by a stationary or moving source, detectable several seconds after the source has passed. Using this information, a single front-mounted whisker enabled a small underwater robot to distinguish between continuing straight and executing a turn, selecting the correct branch in 17 of 20 trials (85.0%) from whisker signals alone. These results connect bioinspired hydrodynamic sensing to robot action and suggest the utility of whiskers for tracking underwater objects.

cs.RO↗

arXiv2026

Long-Reach Robotic Manipulation for Assembly and Outfitting of Lunar Structures

Future infrastructure construction on the lunar surface will require semi- or fully-autonomous operation from robots deployed at the build site. In particular, tasks such as electrical outfitting necessitate transport, routing, and fine manipulation of cables across large structures. To address this need, we present a compact and long-reach manipulator incorporating a deployable composite boom, capable of performing manipulation tasks across large structures and workspaces. We characterize the deflection, vibration, and blossoming characteristics inherent to the deployable structure, and present a manipulation control strategy to mitigate these effects. Experiments indicate an average endpoint accuracy error of less than 15 mm for boom lengths up to 1.8 m. We demonstrate the approach with a cable routing task to illustrate the potential for lunar outfitting applications that benefit from long reach.

cs.RO↗

arXiv2026

Long-Reach Robotic Cleaning for Lunar Solar Arrays

Commercial lunar activity is accelerating the need for reliable surface infrastructure and routine operations to keep it functioning. Maintenance tasks such as inspection, cleaning, dust mitigation, and minor repair are essential to preserve performance and extend system life. A specific application is the cleaning of lunar solar arrays. Solar arrays are expected to provide substantial fraction of lunar surface power and operate for months to years, supplying continuous energy to landers, habitats, and surface assets, making sustained output mission-critical. However, over time lunar dust accumulates on these large solar arrays, which can rapidly degrade panel output and reduce mission lifetime. We propose a small mobile robot equipped with a long-reach, lightweight deployable boom and interchangeable cleaning tool to perform gentle cleaning over meter-scale workspaces with minimal human involvement. Building on prior vision-guided long-reach manipulation, we add a compliant wrist with distal force sensing and a velocity-based admittance controller to regulate stable contact during surface cleaning. In preliminary benchtop experiments on a planar surface, the system maintained approximately 2 N normal force while executing a simple cleaning motion over boom lengths from 0.3 m to 1.0 m, with RMS force error of approximately 0.2 N after initial contact. These early results suggest that deployable long-reach manipulators are a promising architecture for robotic maintenance of lunar infrastructure such as solar arrays, radiators, and optical surfaces.

cs.RO↗

arXiv2026

UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation

Underwater robotic grasping is difficult due to degraded, highly variable imagery and the expense of collecting diverse underwater demonstrations. We introduce a system that (i) autonomously collects successful underwater grasp demonstrations via a self-supervised data collection pipeline and (ii) transfers grasp knowledge from on-land human demonstrations through a depth-based affordance representation that bridges the on-land-to-underwater domain gap and is robust to lighting and color shift. An affordance model trained on on-land handheld demonstrations is deployed underwater zero-shot via geometric alignment, and an affordance-conditioned diffusion policy is then trained on underwater demonstrations to generate control actions. In pool experiments, our approach improves grasping performance and robustness to background shifts, and enables generalization to objects seen only in on-land data, outperforming RGB-only baselines. Code, videos, and additional results are available at https://umi-under-water.github.io.

cs.RO↗