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Camillo Jose Taylor

14 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

Real-Time Glass Detection and Reprojection Using Sensor Fusion Onboard Aerial Robots

Malakhi Hopkins, Varun Murali, Vijay Kumar, Camillo Jose Taylor
ICRA 2026poster

Autonomous aerial robots are increasingly being deployed in real-world scenarios, where transparent obstacles present significant challenges to reliable navigation and mapping. These materials pose a unique problem for traditional perception systems because they lack discernible features and can cau…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations

Bowen Jiang, Yuan Yuan, Xinyi Bai, Zhuoqun Hao, Alyson Yin, Yaojie Hu +3
EMNLP 2025

This work demonstrates that diffusion models can achieve font-controllable multilingual text rendering using just raw images without font label annotations. Visual text rendering remains a significant challenge. While recent methods condition diffusion on glyphs, it is impossible to retrieve exact f

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Towards Rationality in Language and Multimodal Agents: A Survey

Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao, Xinyi Bai +3
NAACL 2025long

This work discusses how to build more rational language and multimodal agents and what criteria define rationality in intelligent systems.Rationality is the quality of being guided by reason, characterized by decision-making that aligns with evidence and logical principles. It plays a crucial role i…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

Vysics: Object Reconstruction Under Occlusion by Fusing Vision and Contact-Rich Physics

Bibit Bianchini, Minghan Zhu, Mengti Sun, Bowen Jiang, Camillo Jose Taylor, Michael Posa
RSS 2025poster

We introduce Vysics, a vision-and-physics framework for a robot to build an expressive geometry and dynamics model of a single rigid body, using a seconds-long RGBD video and the robot’s proprioception. While the computer vision community has built powerful visual 3D perception algorithms, cluttered…

Cited by 1PDF ↗Scholar ↗
2024

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J Su +2
EMNLP 2024main

This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias. We go beyond evaluating LLMs on accuracy; rather, we aim to investigate their token bias in solving logical reasoning tasks. Spec…

Cited by 39Source ↗PDF ↗code ↗Scholar ↗
2024

OCCAM: Online Continuous Controller Adaptation with Meta-Learned Models

Hersh Sanghvi, Spencer Folk, Camillo Jose Taylor
CoRL 2024poster

Control tuning and adaptation present a significant challenge to the usage of robots in diverse environments. It is often nontrivial to find a single set of control parameters by hand that work well across the broad array of environments and conditions that a robot might encounter. Automated adaptat…

Cited by 2Source ↗Scholar ↗
2022

Stronger Together: Air-Ground Robotic Collaboration Using Semantics

Ian D. Miller, Fernando Cladera Ojeda, Trey Smith, Camillo Jose Taylor, Vijay Kumar
RA-L 2022

In this work, we present an end-to-end heterogeneous multi-robot system framework where ground robots are able to localize, plan, and navigate in a semantic map created in real time by a high-altitude quadrotor. The ground robots choose and deconflict their targets independently, without any externa

Cited by 54Source ↗code ↗Scholar ↗
2021

Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR

Ian D. Miller, Anthony Cowley, Ravi Konkimalla, Shreyas S. Shivakumar, Ty Nguyen, Trey Smith +2
RA-L 2021

Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration

Cited by 43Source ↗code ↗Scholar ↗
2020

Mine Tunnel Exploration Using Multiple Quadrupedal Robots

Ian D. Miller, Avraham Cohen, Adarsh Kulkarni, James Laney, Camillo Jose Taylor, Vijay Kumar +8
RA-L 2020

Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility. These challenges are further exacerbated by the need to minimize human intervention for practi

Cited by 108Source ↗Scholar ↗
2018

Experiments in Fast, Autonomous, GPS-Denied Quadrotor Flight

Kartik Mohta, Ke Sun, Sikang Liu, Michael Watterson, Bernd Pfrommer, James Svacha +3
ICRA 2018poster

High speed navigation through unknown environments is a challenging problem in robotics. It requires fast computation and tight integration of all the subsystems on the robot such that the latency in the perception-action loop is as small as possible. Aerial robots add a limitation of payload capaci…

Cited by 61Source ↗Scholar ↗
2018

Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

Ty Nguyen, Steven W. Chen, Shreyas S. Shivakumar, Camillo Jose Taylor, Vijay Kumar
RA-L 2018

Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring. The usage on a robotic system requires a fast and robust homography estimation algorithm. In this letter, we propose an unsupervised learning algorithm t

Cited by 344Source ↗code ↗Scholar ↗