12 citations · 13 across the 3 of their papers we have counts for
8 papers
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
Danny Driess, Jung-Su Ha, Marc Toussaint +1
This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene.…
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy…
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
Danny Driess, Jung-Su Ha, Marc Toussaint
In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP…
Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
Jung-Su Ha, Danny Driess, Marc Toussaint
Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects…
Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning
Marc Toussaint, Jung-Su Ha, Danny Driess
Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models…
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
Soon-Seo Park, Youngjae Min, Jung-Su Ha +2
This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of h…