← Search

Yogesh A. Girdhar

4 accepted papers

2025

Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-Driven Autonomous Underwater Vehicles

ICRA 2025

Controlling AUVs can be challenging because of the effect of complex non-linear hydrodynamic forces acting on the robot, which are significant in water and cannot be ignored. The problem is exacerbated for small AUVs for which the dynamics can change significantly with payload changes and deployment

Cited by 14SourceScholar
2025

SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model

ICRA 2025

We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both range and color dependent effects on image capture.

Cited by 38SourcecodeScholar
2019

Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

RA-L 2019

We present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> lume <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> ocalization under <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli

Cited by 52SourceScholar
2016

Anomaly detection in unstructured environments using Bayesian nonparametric scene modeling

ICRA 2016

This paper explores the use of a Bayesian nonparametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect a

Cited by 19SourceScholar