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Daniel Martin

4 accepted papers

2026

FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction

Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte, Rochishnu Chowdhury, Rushil Paresh Desai, Wuyang Chen +2
CVPR 2026

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train

Cited by 0Source ↗code ↗Scholar ↗
2023

Athletic Mobile Manipulator System for Robotic Wheelchair Tennis

Zulfiqar Zaidi, Daniel Martin, Nathaniel Belles, Viacheslav Zakharov, Arjun Krishna, Kin Man Lee +17
RA-L 2023

Athletics are a quintessential and universal expression of humanity. From French monks who in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12{\text{th}}$</tex-math></inline-formula> century invented <italic

Cited by 25Source ↗code ↗Scholar ↗
2023

DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation

Sravan Jayanthi, Letian Chen, Nadya Balabanska, Van Duong, Erik Scarlatescu, Ezra Ameperosa +5
CoRL 2023poster

Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-error learning is infeasible or specifying a cost function is difficult, such as robotic surgery, autonomous driving, and path-finding for NASA's Mars rovers. However, two key problems remain challenging in OLfD: 1) h…

Cited by 5Source ↗Scholar ↗
2022

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Letian Chen, Sravan Jayanthi, Rohan R Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay
CoRL 2022poster

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deplo…

Cited by 21Source ↗Scholar ↗