Peter David Fagan

I am currently pursuing a PhD in Robotics and Autonomous Systems at the University of Edinburgh and the Edinburgh Centre for Robotics. Under the supervision of Professor Subramanian Ramamoorthy, I am a member of the Robust Autonomy and Decisions (RAD) group.

Prior to my PhD, I completed graduate studies at Stanford University as a Non-Degree Option (NDO) graduate student and earned an undergraduate degree in Mathematics from University College Cork.

Research Focus and Collaboration

I'm currently focused on embodied AI for human-robot interaction tasks, and developing robust policies for control in uncertain, dynamic environments. My recent work investigates the role of Echo State Networks in neural architectures for robot learning.

More broadly, I’m interested in understanding the dynamics of intelligent systems and designing neural architectures inspired by insights into the dynamics of the human brain.

👉 For more info or to explore potential collaborations, feel free to check out my collaboration form .

I’m most engaged by work that aligns with values of integrity, scientific rigor, and a commitment to building systems that respect and enhance human wellbeing. To that end, I’m especially motivated by projects that focus on assistive autonomy in healthcare and human-centered settings.

First-Author Publications

Peter David Fagan, Subramanian Ramamoorthy

Preprint, 2024

Project Page / arXiv

We introduce a new recurrent neural network layer that incorporates fixed nonlinear dynamics models where the dynamics satisfy the Echo State Property. We show that this neural network layer is well suited to the task of overcoming compounding errors under the learning from demonstration paradigm. Through evaluating neural network architectures with/without our layer on the task of reproducing human handwriting traces we show that the introduced neural network layer improves task precision and robustness to perturbations all while maintaining a low computational overhead.

Collaborative Publications

Rimvydas Rubavicius, Peter David Fagan, Alex Lascarides, Subramanian Ramamoorthy

Preprint, 2025

Project Page / arXiv

In this work, we introduce an interactive task learning framework to cope with unforeseen possibilities by exploiting the formal semantic analysis of embodied conversation.

The DROID Dataset Team

Robotics: Science and Systems (R:SS), 2024

Project Page / arXiv

In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset comprising 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 86 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months.

Open X-Embodiment Team

IEEE International Conference on Robotics and Automation (ICRA), May 2024

Project Page / arXiv

In this work, we introduce Open X-Embodiment, a comprehensive collection of robotic learning datasets and RT-X models. These datasets and models facilitate research in embodied AI by providing large-scale, diverse, and realistic environments for training robotic systems. The datasets cover a wide range of tasks and scenarios, enabling robots to learn complex behaviors through interaction with their environment.

Software

Peter David Fagan

Google Summer of Code, 2022

Code

This is the official Python binding for the MoveIt 2 library.