4 papers
Non-Prehensile Throwing: A Reinforcement Learning Perspective
Abdullah Mustafa, Ryo Hanai, Ixchel G. Ramirez-Alpizar +4
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-based) throwing works well for g…
Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors
Ryo Hanai, Yukiyasu Domaea, Ixchel G. Ramirez-Alpizar +3
Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robots with a similar ability. T…
NeuralMeshing: Complete Object Mesh Extraction from Casual Captures
Floris Erich, Naoya Chiba, Abdullah Mustafa +4
How can we extract complete geometric models of objects that we encounter in our daily life, without having access to commercial 3D scanners? In this paper we present an automated…
Visual Imitation Learning of Non-Prehensile Manipulation Tasks with Dynamics-Supervised Models
Abdullah Mustafa, Ryo Hanai, Ixchel Ramirez +4
Unlike quasi-static robotic manipulation tasks like pick-and-place, dynamic tasks such as non-prehensile manipulation pose greater challenges, especially for vision-based control.…