Thomas A. Funkhouser — accepted AI-conference papers

2022

Learning Pneumatic Non-Prehensile Manipulation With a Mobile Blower

Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Szymon Rusinkiewicz, Thomas A. Funkhouser

RA-L 2022

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must i) continually adapt to unexpected changes from its actions, ii) maintain fine-

Cited by 10 Source ↗code ↗Scholar ↗

2020

Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations

Shuran Song, Andy Zeng, Johnny Lee, Thomas A. Funkhouser

RA-L 2020

Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective training data and learning efficiently, most grasping algorithms today are limit

Cited by 266 Source ↗Scholar ↗