4 papers · 1 filter
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…
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…
Learning Implicit Sampling Distributions for Motion Planning
Clark Zhang, Jinwook Huh, Daniel D. Lee
Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficie…