Mateo Guaman Castro

I am a PhD student at the University of Washington working on Robotics and Machine Learning, advised by Prof. Byron Boots and Prof. Abhishek Gupta.

Previously, I was a Master's student in Robotics in the Robotics Institute at Carnegie Mellon University, advised by Prof. Sebastian Scherer as a member of the AirLab and the Field Robotics Center. Before that, I studied electrical engineering at Tufts Univesity, where I recieved my BS.

Email  /  CV (Oct. 2025) /  Google Scholar  /  Twitter  /  Github

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Research

I am interested in reinforcement learning and representation learning for fast adaptation and generalization in messy environments. My ultimate goal is to develop robots that adapt fast during deployment, probe when uncertain, and make mistakes only once, if at all.

VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation
Mateo Guaman Castro, Sidharth Rajagopal, Daniel Gorbatov, Matthew Schmittle, Rohan Baijal, Octi Zhang, Rosario Scalise, Sidharth Talia, Emma Romig, Celso de Melo, Byron Boots, Abhishek Gupta,
Submitted to International Conference on Robotics and Automation (ICRA), 2026
Oral at the CoRL 2025 Workshop on Generalist Policies in the Wild & Robo-Arena Challenge
pdf / project page

VAMOS is a hierarchical vision-language-action model that decouples semantic planning from embodiment grounding, enabling robust cross-embodiment navigation with natural language steerability.

Long Range Navigator: Extending robot planning horizons beyond metric maps
Matthew Schmittle, Rohan Baijal, Nathan Hatch, Rosario Scalise, Mateo Guaman Castro Sidharth Talia, Khimya Khetarpal, Siddhartha Srinivasa, Byron Boots,
Conference on Robot Learning (CoRL), 2025
Best Paper Award, RSS 2025 Workshop on Resilient Off-road Autonomous Robotics
arXiv / pdf / project page

Long Range Navigator enables robots to look beyond local maps through affordances in image space.

Agile Continuous Jumping in Discontinuous Terrains
Yuxiang Yang, Guanya Shi, Changyi Lin, Xiangyun Meng, Rosario Scalise, Mateo Guaman Castro, Wenhao Yu, Tingnan Zhang, Ding Zhao, Jie Tan, Byron Boots
International Conference on Robotics and Automation (ICRA), 2025
arXiv / pdf / project page

We developed a system that enables quadrupedal robots to perform continuous, precise jumps across challenging terrains like stairs and stepping stones, achieving unprecedented agility.

DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset
DROID Dataset Team
Robotics: Science and Systems (RSS), 2024
arXiv / pdf / project page

We present a large dataset for robot learning.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration
International Conference on Robotics and Automation (ICRA), 2024
IEEE ICRA Best Conference Paper Award
arXiv / pdf / project page

We present a large dataset for robot learning.

TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
Matthew Sivaprakasam, Parv Maheshwari, Mateo Guaman Castro, Samuel Triest, Micah Nye, Steven Willits, Andrew Saba, Wenshan Wang, Sebastian Scherer
International Conference on Robotics and Automation (ICRA), 2024
arXiv / pdf / IEEE / project page / video

We present a dataset for off-road driving with multiple modalities at high speed.

How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Mateo Guaman Castro, Samuel Triest, Wenshan Wang, Jason M. Gregory, Felix Sanchez, John G. Rogers III, Sebastian Scherer
International Conference on Robotics and Automation (ICRA), 2023
arXiv / pdf / IEEE / project page / short video / long video

We propose a self-supervised method to predict traversability costmaps by combining exteroceptive environmental information with proprioceptive terrain interaction feedback.

Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
Samuel Triest, Mateo Guaman Castro, Parv Maheshwari, Matthew Sivaprakasam, Wenshan Wang, Sebastian Scherer
International Conference on Robotics and Automation (ICRA), 2023
arXiv / pdf / IEEE

We present an inverse reinforcement learning-based method that efficiently predicts uncertainty-aware costmaps for off-road traversability via conditional value-at-risk (CVaR).

TartanDrive 1.5: Improving Large Multimodal Robotics Dataset Collection and Distribution
Matthew Sivaprakasam, Samuel Triest, Mateo Guaman Castro, Micah Nye, Mukhtar Maulimov, Cherie Ho, Parv Maheshwari, Wenshan Wang, Sebastian Scherer
Workshop on Pretraining for Robotics (PT4R), International Conference on Robotics and Automation (ICRA), 2023
pdf

In this work we discuss the improvements to our previous dataset, TartanDrive.

Toward Life-Long Creative Problem Solving: Using World Models for Increased Performance in Novelty Resolution
Evana Gizzi, Wo Wei Lin, Mateo Guaman Castro, Ethan Harvey, Jivko Sinapov
International Conference on Computational Creativity (ICCC), 2022
pdf

In this work, we investigate methods for life-long creative problem solving (LLCPS), with the goal of increasing CPS capability over time.

A Novelty-Centric Agent Architecture for Changing Worlds
Faizan Muhammad, Vasanth Sarathy, Gyan Tatiya, Shivam Goel, Saurav Gyawali, Mateo Guaman Castro, Jivko Sinapov, Matthias Scheutz
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021
pdf

We present a formal framework and implementation in a cognitive agent for novelty handling and demonstrate the efficacy of the proposed methods for detecting and handling a large set of novelties in a crafting task in a simulated environment.

A Framework for Creative Problem Solving Through Action Discovery
Evana Gizzi, Mateo Guaman Castro, Wo Wei Lin, Jivko Sinapov
Workshop on Declarative and Neurosymbolic Representations in Robot Learning and Control, Robotics: Science and Systems (RSS), 2021
pdf

We introduce a unified framework for creative problem solving through action discovery. We describe two methods which enable action discovery at a declarative and neurosymbolic level, namely through action primitive segmentation, and behavior babbling, respectively.

Creative Problem Solving by Robots Using Action Primitive Discovery
Evana Gizzi, Mateo Guaman Castro, Jivko Sinapov
International Conference on Development and Learning (ICDL), 2019
pdf / IEEE

We describe a method for discovering new action primitives through object exploration and action segmentation, which is able to iteratively update the robot's knowledge base on-the-fly until the solution becomes feasible.



Website template from Jon Barron.