7 papers
Self-supervised Wide Baseline Visual Servoing via 3D Equivariance
Jinwook Huh, Jungseok Hong, Suveer Garg +2
One of the challenging input settings for visual servoing is when the initial and goal camera views are far apart. Such settings are difficult because the wide baseline can cause d…
Learning Continuous Cost-to-Go Functions for Non-holonomic Systems
Jinwook Huh, Daniel D. Lee, Volkan Isler
This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from info…
Cost-to-Go Function Generating Networks for High Dimensional Motion Planning
Jinwook Huh, Volkan Isler, Daniel D. Lee
This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over t…
Learning to Generate Cost-to-Go Functions for Efficient Motion Planning
Jinwook Huh, Galen Xing, Ziyun Wang +2
Traditional motion planning is computationally burdensome for practical robots, involving extensive collision checking and considerable iterative propagation of cost values. We pre…
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…
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…