Peter David Fagan

Learning from Demonstration with Implicit Nonlinear Dynamics Models

Peter David Fagan, Subramanian Ramamoorthy

Placeholder, 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.

DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset

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: Robotic Learning Datasets and RT-X Models

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.