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