3 citations · 4 across the 8 of their papers we have counts for
8 papers
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
Minjong Yoo, Sangwoo Cho, Honguk Woo
Reinforcement learning (RL) with diverse offline datasets can have the advantage of leveraging the relation of multiple tasks and the common skills learned across those tasks, henc…
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
Woo Kyung Kim, Minjong Yoo, Honguk Woo
Data-driven offline reinforcement learning and imitation learning approaches have been gaining popularity in addressing sequential decision-making problems. Yet, these approaches r…
Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks
Donghoon Kim, Minjong Yoo, Honguk Woo
Goal-conditioned (GC) policy learning often faces a challenge arising from the sparsity of rewards, when confronting long-horizon goals. To address the challenge, we explore skill-…
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments
Sangwoo Shin, Seunghyun Kim, Youngsoo Jang +2
In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill…
Model Adaptation for Time Constrained Embodied Control
Jaehyun Song, Minjong Yoo, Honguk Woo
When adopting a deep learning model for embodied agents, it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can b…
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
Sangwoo Shin, Daehee Lee, Minjong Yoo +2
One-shot imitation is to learn a new task from a single demonstration, yet it is a challenging problem to adopt it for complex tasks with the high domain diversity inherent in a no…