4 citations · 4 across the 3 of their papers we have counts for
3 papers
Online Tool Selection with Learned Grasp Prediction Models
Khashayar Rohanimanesh, Jake Metzger, William Richards +1
Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped w…
Self-Supervised Goal-Conditioned Pick and Place
Coline Devin, Payam Rowghanian, Chris Vigorito +2
Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world. However, it is often not obvious \emph{how} to learning from auto…
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping
Ben Goodrich, Alex Kuefler, William D. Richards
Accurate depth estimation remains an open problem for robotic manipulation; even state of the art techniques including structured light and LiDAR sensors fail on reflective or tran…