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Carlos Nieto-Granda

13 accepted papers

2026

Air-Ground Collaboration for Language-Specified Missions in Unknown Environments (I)

Fernando Cladera, Zachary Ravichandran, Jason Hughes, Varun Murali, Carlos Nieto-Granda, M. Ani Hsieh +3
ICRA 2026poster

As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming si…

Cited by 0Scholar ↗
2026

BEASST: Behavioral Entropic Gradient Based Adaptive Source Seeking for Mobile Robots

Donipolo Ghimire, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia
RA-L 2026

This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized sign

Cited by 1Source ↗Scholar ↗
2026

Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation

Nirabhra Mandal, Aamodh Suresh, Carlos Nieto-Granda, Sonia Martínez
RA-L 2026

We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using <i

Cited by 1Source ↗Scholar ↗
2026

NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration

Yang Zhou, Yash Shetye, Long Quang, Devon Super, Jesse Milzman, Manohari Goarin +5
RA-L 2026

Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack of a unified execution stack. This paper presents NeuroMesh, a multi-domain, cross-platform, and modular decentralized neural inference framework th

Cited by 0Source ↗Scholar ↗
2026

Safe and Dynamically-Feasible Motion Planning Using Control Lyapunov and Barrier Functions

Pol Mestres, Carlos Nieto-Granda, Jorge Cortes
ICRA 2026poster

This paper considers the problem of designing motion planning algorithms for control-affine systems that generate collision-free paths from an initial to a final destination and can be executed using safe and dynamically-feasible controllers. We introduce the C-CLF-CBF-RRT algorithm, which produces …

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning

Wesley A. Suttle, Aamodh Suresh, Carlos Nieto-Granda
ICLR 2025poster

Entropy-based objectives are widely used to perform state space exploration in reinforcement learning (RL) and dataset generation for offline RL. Behavioral entropy (BE), a rigorous generalization of classical entropies that incorporates cognitive and perceptual biases of agents, was recently propos…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments

Yang Zhou, Long Quang, Carlos Nieto-Granda, Giuseppe Loianno
RA-L 2024

In the past decade, although single-robot perception has made significant advancements, the exploration of multi-robot collaborative perception remains largely unexplored. This involves fusing compressed, intermittent, limited, heterogeneous, and asynchronous environmental information across multipl

Cited by 9Source ↗code ↗Scholar ↗
2024

Distributed Safe Navigation of Multi-Agent Systems Using Control Barrier Function-Based Controllers

Pol Mestres, Carlos Nieto-Granda, Jorge Cortés
RA-L 2024

This letter proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance with obstacles and teammates as constraints on the control input for a state-dependent network optimization problem tha

Cited by 34Source ↗Scholar ↗
2023

Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned

Yulun Tian, Yun Chang, Long Quang, Arthur Schang, Carlos Nieto-Granda, Jonathan P. How +1
IROS 2023poster

This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-sca…

Cited by 40Source ↗Scholar ↗
2019

DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

Anwesan Pal, Carlos Nieto-Granda, Henrik I. Christensen
IROS 2019poster

In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires acces…

Cited by 31Source ↗Scholar ↗
2017

Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

Stephanie Kemna, John G. Rogers, Carlos Nieto-Granda, Stuart Young, Gaurav S. Sukhatme
ICRA 2017poster

Autonomous underwater vehicles (AUVs) are cost- and time-efficient systems for environmental sampling. Informative adaptive sampling has been shown to be an effective method of sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative ad…

Cited by 113Source ↗Scholar ↗
2016

Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach

Siddharth Choudhary, Luca Carlone, Carlos Nieto-Granda, John G. Rogers III, Henrik I. Christensen, Frank Dellaert
ICRA 2016

We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results [1] which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main con

Cited by 44Source ↗Scholar ↗