Zachary Goddard — accepted AI-conference papers

2022

Adversarial Sampling-Based Motion Planning

Hayden Nichols, Mark Jimenez, Zachary Goddard, Michael Sparapany, Byron Boots, Anirban Mazumdar

RA-L 2022

There are many scenarios in which a mobile agent may not want its path to be predictable. Examples include preserving privacy or confusing an adversary. However, this desire for deception can conflict with the need for a low path cost. Optimal plans such as those produced by RRT* may have low path c

Cited by 11 Source ↗Scholar ↗

2020

High Force Density Gripping with UV Activation and Sacrificial Adhesion

Esther Lee, Zachary Goddard, Joshua Ngotiaoco, Noe Monterrosa, Anirban Mazumdar

ICRA 2020poster

This paper presents a novel physical gripping framework intended for controlled, high force density attachment on a range of surfaces. Our framework utilizes a light-activated chemical adhesive to attach to surfaces. The cured adhesive is part of a "sacrificial layer," which is shed when the gripper…

Cited by 0 Source ↗Scholar ↗