23 citations · 80 across the 17 of their papers we have counts for
17 papers
Learning Feasibility of Factored Nonlinear Programs in Robotic Manipulation Planning
Joaquim Ortiz-Haro, Jung-Su Ha, Danny Driess +2
A factored Nonlinear Program (Factored-NLP) explicitly models the dependencies between a set of continuous variables and nonlinear constraints, providing an expressive formulation…
Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation
Marc Toussaint, Jason Harris, Jung-Su Ha +2
Task and Motion Planning has made great progress in solving hard sequential manipulation problems. However, a gap between such planning formulations and control methods for reactiv…
Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input
Jung-Su Ha, Danny Driess, Marc Toussaint
Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or mo…
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…
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e.g., video. The framework…
Approximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
Jung-Su Ha, Hyeok-Joo Chae, Han-Lim Choi
This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration…
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
Jung-Su Ha, Han-Lim Choi
This work presents a multiscale framework to solve an inverse reinforcement learning (IRL) problem for continuous-time/state stochastic systems. We take advantage of a diffusion wa…
Informative Path Planning and Mapping with Multiple UAVs in Wind Fields
Doo-Hyun Cho, Jung-Su Ha, Sujin Lee +2
Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating u…
Multiscale Abstraction, Planning and Control using Diffusion Wavelets for Stochastic Optimal Control Problems
Jung-Su Ha, Han-Lim Choi
This work presents a multiscale framework to solve a class of stochastic optimal control problems in the context of robot motion planning and control in a complex environment. In o…
Iterative Methods for Efficient Sampling-Based Optimal Motion Planning of Nonlinear Systems
Jung-Su Ha, Han-Lim Choi, Jeong hwan Jeon
This paper extends the RRT* algorithm, a recently developed but widely-used sampling-based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints.…
Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment
Jung-Su Ha, Soon-Seo Park, Han-Lim Choi
This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by develop…
A Topology-Guided Path Integral Approach for Stochastic Optimal Control
Jung-Su Ha, Han-Lim Choi
This work presents an efficient method to solve a class of continuous-time, continuous-space stochastic optimal control problems of robot motion in a cluttered environment. The met…