David Cordova Bulens — accepted AI-conference papers

2024

Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies

Qiang Wang, Robert McCarthy, David Cordova Bulens, Francisco Roldan Sanchez, Kevin McGuinness, Noel E. O'Connor +1

RA-L 2024

This letter presents our solution for the Real Robot Challenge III <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , aiming to address dexterous robotic manipulation tasks through learning from offline data. In this competition, participants wer

Cited by 13 Source ↗code ↗Scholar ↗

2024

Robust Learning-Based Incipient Slip Detection Using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping

Qiang Wang, Pablo Martinez Ulloa, Robert Burke, David Cordova Bulens, Stephen J. Redmond

RA-L 2024

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a signific

Cited by 9 Source ↗Scholar ↗

2023

Improving Behavioural Cloning with Positive Unlabeled Learning

Qiang Wang, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel E. O’Connor, Francisco Roldan Sanchez +3

CoRL 2023poster

Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demons…

Cited by 8 Source ↗Scholar ↗