← Search

Soon-Seo Park

2 accepted papers

2021

Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning

ICRA 2021poster

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 for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by representa…

Cited by 3SourceScholar
2018

Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems

NeurIPS 2018poster

We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e.g., video. The framework builds upon recent advances in amortized inference methods that use both an inference network and a refinement procedure to outpu…