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Fernando Cladera

9 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

HALO: Language-Conditioned Overhead Monocular Aerial Exploration and Navigation

Yuezhan Tao, Dexter Ong, Fernando Cladera, Jason Hughes, Camillo J. Taylor, Pratik Chaudhari +1
RA-L 2026

We demonstrate real-time overhead aerial metric-semantic mapping and exploration using a monocular camera paired with a global positioning system (GPS). Our system, named HALO, addresses two key challenges: (i) real-time dense 3D reconstruction using vision at large distances, and (ii) mapping and e

Cited by 0Source ↗Scholar ↗
2025

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention

Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
CoRL 2025poster

Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or i…

Cited by 0Source ↗Scholar ↗
2025

EvMAPPER: High-Altitude Orthomapping with Event Cameras

Fernando Cladera, Kenneth Chaney, M. Ani Hsieh, Camillo J. Taylor, Vijay Kumar
ICRA 2025

Traditionally, unmanned aerial vehicles (UAVs) rely on CMOS-based cameras to collect images about the world below. One of the most successful applications of UAVs is to generate orthomosaics or orthomaps, in which a series of images are integrated to develop a larger map. However, using CMOS-based c

Cited by 3Source ↗Scholar ↗
2024

Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications

Fernando Cladera, Zachary Ravichandran, Ian D. Miller, M. Ani Hsieh, C. J. Taylor, Vijay Kumar
ICRA 2024poster

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-r…

Cited by 7Source ↗Scholar ↗
2024

TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards

Derek Cheng, Fernando Cladera, Ankit Prabhu, Xu Liu, Alan Zhu, P. Corey Green +3
ICRA 2024poster

Data collection for forestry, timber, and agriculture relies on manual techniques which are labor-intensive and time-consuming. We seek to demonstrate that robotics offers improvements over these techniques and can accelerate agricultural research, beginning with semantic segmentation and diameter e…

Cited by 11Source ↗code ↗Scholar ↗
2023

Active Metric-Semantic Mapping by Multiple Aerial Robots

Xu Liu, Ankit Prabhu, Fernando Cladera, Ian D. Miller, Lifeng Zhou, Camillo J. Taylor +1
ICRA 2023poster

Traditional approaches for active mapping focus on building geometric maps. For most real-world applications, however, actionable information is related to semantically meaningful objects in the environment. We propose an approach to the active metric-semantic mapping problem that enables multiple h…

Cited by 24Source ↗Scholar ↗
2023

SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

Yuezhan Tao, Yuwei Wu, Beiming Li, Fernando Cladera, Alex Zhou, Dinesh Thakur +1
ICRA 2023poster

We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, sa…

Cited by 46Source ↗code ↗Scholar ↗
2021

Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors

Anthony Bisulco, Fernando Cladera, Volkan Isler, Daniel D. Lee
ICRA 2021poster

This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high-speed motion. As a representative scenario we consider a robot at rest, reacting to a small, fast approaching object at speeds higher than 15 m/s. Since conventional image sensors at typical frame rates observe s…

Cited by 12Source ↗Scholar ↗