Using Simulations to Aid Robot Learning
Simulations are the primary way for a robot to predict actions and safely learn. This project investigates how we can train robot task models using simulations, and how those models can be helpful for human teachers and operators.
TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer
A. Allevato, E. S. Short, M. Pryor, A. L. Thomaz
Conference paper, CoRL 2019
PDF | bibtex | video | code
3D Computer Vision for Robotics
Using Point Cloud Library and OpenCV, I developed a 3D pose estimation library based on Robot Operating System (ROS). I showed that the library was effective for use in robotic sorting, remote inspection, and small part picking. We've since found it useful for quickly adding visual object detection capability to various robot experiments.
Glovebox Workcell Manufacturing and Integration
In this three-year project, our team at the Nuclear and Applied Robotics Group (NRG) deployed robots in various configurations inside nuclear gloveboxes. Developed in conjunction with Los Alamos National Laboratory, we investigated visual recognition, mixed-waste sorting, and manufacturing tasks such as automated hole drilling with a highly customized robotic system
Automated Glovebox Workcell Design
E. Paredes, C. Petlowany, M. Horn, A. Allevato, M. Pryor
Conference paper, WMS 2018
Choosing Observation Points for a Camera Robot
We developed an algorithm to select a viewpoint from which to observe a task. This can be useful in a two-robot scenario where one robot is the “cameraman” and the other is the “worker” or “actor.” The camera viewpoint is chosen so as to stay out of the actor’s way, while still providing a good view of the task region.