4 citations · 4 across the 13 of their papers we have counts for
14 papers
Localization in Spatiotemporal Fields via Environmental PDEs
Jose Fuentes, Abdullah Al Redwan Newaz, Ana Cavalcanti +1
This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signatures. Two PDE classes are consi…
A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM
Joao Victor T. Borges, Fabio Coelho, Paulo Padrao +4
This paper presents a new implementation of the NeoSLAM algorithm. The proposed version is a complete rewrite of NeoSLAM into a modular architecture using modern frameworks that, t…
BOWConnect: Parallel Bayesian Optimization over Windows with Learned Local Cost Maps for Sample-Efficient Kinodynamic Motion Planning
Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +1
This paper presents BOWConnect, a bidirectional parallel kinodynamic motion planner that addresses three fundamental limitations of existing sampling-based methods: sample ineffici…
Muninn: Your Trajectory Diffusion Model But Faster
Gokul Puthumanaillam, Hao Jiang, Ruben Hernandez +4
Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohibitively slow. Existing acce…
Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution
Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +3
We address multi-robot motion planning under Signal Temporal Logic (STL) specifications with kinodynamic constraints. Exact approaches face scalability bottlenecks and limited adap…
CyberLLM-FINDS 2025: Instruction-Tuned Fine-tuning of Domain-Specific LLMs with Retrieval-Augmented Generation and Graph Integration for MITRE Evaluation
Vasanth Iyer, Leonardo Bobadilla, S. S. Iyengar
Large Language Models (LLMs) such as Gemma-2B have shown strong performance in various natural language processing tasks. However, general-purpose models often lack the domain expe…
Bioinspired SLAM Approach for Unmanned Surface Vehicle
Fabio Coelho, Joao Victor T. Borges, Paulo Padrao +4
This paper presents OpenRatSLAM2, a new version of OpenRatSLAM - a bioinspired SLAM framework based on computational models of the rodent hippocampus. OpenRatSLAM2 delivers low-com…
Online Learning of Deceptive Policies under Intermittent Observation
Gokul Puthumanaillam, Ram Padmanabhan, Jose Fuentes +7
In supervisory control settings, autonomous systems are not monitored continuously. Instead, monitoring often occurs at sporadic intervals within known bounds. We study the problem…
Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
Gokul Puthumanaillam, Aditya Penumarti, Manav Vora +5
Robots equipped with rich sensor suites can localize reliably in partially-observable environments, but powering every sensor continuously is wasteful and often infeasible. Belief-…
BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
Sourav Raxit, Abdullah Al Redwan Newaz, Paulo Padrao +2
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO).…
Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection
Daniel Correa, Tero Kaarlela, Jose Fuentes +3
This paper presents a reinforcement learning (RL) environment for developing an autonomous underwater robotic coral sampling agent, a crucial coral reef conservation and research t…
Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +2
Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels o…
TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Models
Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes +6
Predicting the near-term behavior of a reactive agent is crucial in many robotic scenarios, yet remains challenging when observations of that agent are sparse or intermittent. Visi…
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin +10
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…