4 citations · 11 across the 8 of their papers we have counts for
8 papers
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…
Nearest neighbor density functional estimation from inverse Laplace transform
J. Jon Ryu, Shouvik Ganguly, Young-Han Kim +2
A new approach to -consistent estimation of a general density functional using -nearest neighbor distances is proposed, where the functional under consideration is in the f…
Belief Flows of Robust Online Learning
Pedro A. Ortega, Koby Crammer, Daniel D. Lee
This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function. Similar to p…