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20162022
most citedApproximate Inference-based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models

15 citations · 26 across the 5 of their papers we have counts for

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cs.LG2020

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…

cs.LG2018★ 11 cited

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…