23 citations · 80 across the 17 of their papers we have counts for
12 papers · 1 filter
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.…
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…
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…
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…