A successive approximation-based approach for optimal kinodynamic motion planning with nonlinear differential constraints. - Jung-Su Ha; Ju-Jang Lee; Han-Lim Choi (CDC, 2013)

Periodic sensing trajectory generation for persistent monitoring. - Jung-Su Ha; Han-Lim Choi (CDC, 2014)

A stochastic game-theoretic approach for analysis of multiple cooperative air combat. - Jung-Su Ha; Hyeok-Joo Chae; Han-Lim Choi (ACC, 2015)

Informative windowed forecasting of continuous-time linear systems for mutual information-based sensor planning. - Han-Lim Choi; Jung-Su Ha (Autom., 2015)

An Efficient Path Integral Approach for Stochastic Optimal Control with a Topology-Embedded Sampling-Based Planner. - Jung-Su Ha; Han-Lim Choi (CoRR, 2015)

Informative Path Planning and Mapping with Multiple UAVs in Wind Fields. - Doo-Hyun Cho; Jung-Su Ha; Su-Jin Lee; Sunghyun Moon; Han-Lim Choi (DARS, 2016)

A topology-guided path integral approach for stochastic optimal control. - Jung-Su Ha; Han-Lim Choi (ICRA, 2016)

Multiscale Inverse Reinforcement Learning using Diffusion Wavelets. - Jung-Su Ha; Han-Lim Choi (CoRR, 2016)

Asymptotically Optimal Sampling-Based Algorithms for Topological Motion Planning. - Jung-Su Ha; Soon-Seo Park; Han-Lim Choi (CoRR, 2016)

Multiscale abstraction, planning and control using diffusion wavelets for stochastic optimal control problems. - Jung-Su Ha; Han-Lim Choi (ICRA, 2017)

High-dimensional Motion Planning using Latent Variable Models via Approximate Inference. - Jung-Su Ha; Hyeok-Joo Chae; Han-Lim Choi (CoRR, 2017)

Adaptive Path-Integral Approach for Representation Learning and Planning. - Jung-Su Ha; Young-Jin Park; Hyeok-Joo Chae; Soon-Seo Park; Han-Lim Choi (ICLR (Workshop), 2018)

Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems. - Jung-Su Ha; Young-Jin Park; Hyeok-Joo Chae; Soon-Seo Park; Han-Lim Choi (NeurIPS, 2018)

Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems. - Jung-Su Ha; Han-Lim Choi; Jeong hwan Jeon (Int. J. Appl. Math. Comput. Sci., 2018)

Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems. - Jung-Su Ha; Young-Jin Park; Hyeok-Joo Chae; Soon-Seo Park; Han-Lim Choi (CoRR, 2018)

A Distributed ADMM Approach to Informative Trajectory Planning for Multi-Target Tracking. - Soon-Seo Park; Jung-Su Ha; Doo-Hyun Cho; Han-Lim Choi (CoRR, 2018)

Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models. - Jung-Su Ha; Hyeok-Joo Chae; Han-Lim Choi (IEEE Robotics Autom. Lett., 2018)

A Stochastic Game-Based Approach for Multiple Beyond-Visual-Range Air Combat. - Jung-Su Ha; Hyeok-Joo Chae; Han-Lim Choi (Unmanned Syst., 2018)

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking. - Soon-Seo Park; Youngjae Min; Jung-Su Ha; Doo-Hyun Cho; Han-Lim Choi (IEEE Access, 2019)

On periodic optimal solutions of persistent sensor planning for continuous-time linear systems. - Jung-Su Ha; Han-Lim Choi (Autom., 2019)

Topology-guided path integral approach for stochastic optimal control in cluttered environment. - Jung-Su Ha; Soon-Seo Park; Han-Lim Choi (Robotics Auton. Syst., 2019)

Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning. - Danny Driess; Ozgur S. Oguz; Jung-Su Ha; Marc Toussaint (ICRA, 2020)

A Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty. - Jung-Su Ha; Danny Driess; Marc Toussaint (ICRA, 2020)

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 (Robotics: Science and Systems, 2020)

Describing Physics For Physical Reasoning: Force-Based Sequential Manipulation Planning. - Marc Toussaint; Jung-Su Ha; Danny Driess (IEEE Robotics Autom. Lett., 2020)

Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning. - Danny Driess; Jung-Su Ha; Marc Toussaint; Russ Tedrake (CoRL, 2021)

Structured deep generative models for sampling on constraint manifolds in sequential manipulation. - Joaquim Ortiz de Haro; Jung-Su Ha; Danny Driess; Marc Toussaint (CoRL, 2021)

Learning Geometric Reasoning and Control for Long-Horizon Tasks from Visual Input. - Danny Driess; Jung-Su Ha; Russ Tedrake; Marc Toussaint (ICRA, 2021)

Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning. - Jung-Su Ha; Young-Jin Park; Hyeok-Joo Chae; Soon-Seo Park; Han-Lim Choi (ICRA, 2021)

Co-Optimizing Robot, Environment, and Tool Design via Joint Manipulation Planning. - Marc Toussaint; Jung-Su Ha; Ozgur S. Oguz (ICRA, 2021)

Learning Neural Implicit Functions as Object Representations for Robotic Manipulation. - Jung-Su Ha; Danny Driess; Marc Toussaint (CoRR, 2021)

Learning to solve sequential physical reasoning problems from a scene image. - Danny Driess; Jung-Su Ha; Marc Toussaint (Int. J. Robotics Res., 2021)

A diffusion wavelets-based multiscale framework for inverse optimal control of stochastic systems. - Jung-Su Ha; Hyeok-Joo Chae; Han-Lim Choi (Int. J. Syst. Sci., 2021)

Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation. - Marc Toussaint; Jason Harris; Jung-Su Ha; Danny Driess; Wolfgang Hönig (IROS, 2022)

Deep Visual Constraints: Neural Implicit Models for Manipulation Planning From Visual Input. - Jung-Su Ha; Danny Driess; Marc Toussaint (IEEE Robotics Autom. Lett., 2022)

Learning Feasibility of Factored Nonlinear Programs in Robotic Manipulation Planning. - Joaquim Ortiz de Haro; Jung-Su Ha; Danny Driess; Erez Karpas; Marc Toussaint (ICRA, 2023)