4 citations · 5 across the 12 of their papers we have counts for
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cs.RO2019
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner
Tarik Tosun, Eric Mitchell, Ben Eisner +6
We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of hu…
cs.RO2019
Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning
Jinwook Huh, Omur Arslan, Daniel D. Lee
In this paper, we introduce a new probabilistically safe local steering primitive for sampling-based motion planning in complex high-dimensional configuration spaces. Our local ste…