activity
20232026
collaborators

13 papers

cs.LG2026

Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL

Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2

Preference-based reinforcement learning can learn effective reward functions from comparisons, but its scalability is constrained by the high cost of oracle feedback. Lightweight v…

cs.CV2025

Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generation

Sarosij Bose, Ravi K. Rajendran, Biplob Debnath +3

Radiology Report Generation (RRG) is a critical step toward automating healthcare workflows, facilitating accurate patient assessments, and reducing the workload of medical profess…

cs.RO2025

GS-NBV: a Geometry-based, Semantics-aware Viewpoint Planning Algorithm for Avocado Harvesting under Occlusions

Xiao'ao Song, Konstantinos Karydis

Efficient identification of picking points is critical for automated fruit harvesting. Avocados present unique challenges owing to their irregular shape, weight, and less-structure…

cs.CV2025

BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations

Weiduo Yuan, Jerry Li, Justin Yue +3

Accurate LiDAR-camera calibration is fundamental to fusing multi-modal perception in autonomous driving and robotic systems. Traditional calibration methods require extensive data…

cs.CV2025

Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation

Sarosij Bose, Hannah Dela Cruz, Arindam Dutta +3

Human pose estimation is a critical tool across a variety of healthcare applications. Despite significant progress in pose estimation algorithms targeting adults, such developments…

cs.RO2025

SeGuE: Semantic Guided Exploration for Mobile Robots

Cody Simons, Aritra Samanta, Amit K. Roy-Chowdhury +1

The rise of embodied AI applications has enabled robots to perform complex tasks which require a sophisticated understanding of their environment. To enable successful robot operat…

cs.CV2025

Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes

Sarosij Bose, Arindam Dutta, Sayak Nag +4

Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem. Consequently, when the scene is rendered…

cs.LG2025

Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning

Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2

Preference-based reinforcement learning (RL) offers a promising approach for aligning policies with human intent but is often constrained by the high cost of human feedback. In thi…

cs.CV2025

Unsupervised Domain Adaptation for Occlusion Resilient Human Pose Estimation

Arindam Dutta, Sarosij Bose, Saketh Bachu +3

Occlusions are a significant challenge to human pose estimation algorithms, often resulting in inaccurate and anatomically implausible poses. Although current occlusion-robust huma…

cs.LG2024

Robust Offline Imitation Learning from Diverse Auxiliary Data

Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2

Offline imitation learning enables learning a policy solely from a set of expert demonstrations, without any environment interaction. To alleviate the issue of distribution shift a…

cs.RO2024

Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments

Cody Simons, Zhichao Liu, Brandon Marcus +2

In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's cur…

cs.RO2024

Vision-based Xylem Wetness Classification in Stem Water Potential Determination

Pamodya Peiris, Aritra Samanta, Caio Mucchiani +3

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant s…

cs.CV2023

SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets

Cody Simons, Dripta S. Raychaudhuri, Sk Miraj Ahmed +3

Scene understanding using multi-modal data is necessary in many applications, e.g., autonomous navigation. To achieve this in a variety of situations, existing models must be able…