2 citations · 2 across the 8 of their papers we have counts for
9 papers
BooST: Bridging Semantics and Motions for Efficient Skill Transfer
Jusuk Lee, Daesol Cho, Jonghun Shin +4
Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot l…
ObsGraph: Hierarchical Observation Representation for Embodied Reasoning and Exploration
Taekbeom Lee, Youngseok Jang, Jeonghwa Heo +2
Embodied reasoning and exploration are increasingly considered crucial abilities for robots operating in complex and unfamiliar environments. To accomplish tasks in such settings,…
: A Scalable 3D Interaction-Trace World Model
Seungjae Lee, Yoonkyo Jung, Jusuk Lee +6
World models that capture how actions induce physical change enable scalable robot learning without reliance on embodiment-specific action labels. Pixel-space video models provide…
Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires
Yongjae Lim, Dabin Kim, H. Jin Kim
Driving without considering the preferred separation distance from surrounding vehicles may cause discomfort for users. To address this limitation, we propose a planning framework…
SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis
Jeongjun Choi, Yeonsoo Park, H. Jin Kim
We present SceneNAT, a masked non-autoregressive Transformer for 3D indoor scene synthesis from natural language instructions. It generates complete scenes in a few parallel decodi…
Periodic Skill Discovery
Jonghae Park, Daesol Cho, Jusuk Lee +3
Unsupervised skill discovery in reinforcement learning (RL) aims to learn diverse behaviors without relying on external rewards. However, current methods often overlook the periodi…
Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning
Gawon Lee, Daesol Cho, H. Jin Kim
Multi-task reinforcement learning (MTRL) offers a promising approach to improve sample efficiency and generalization by training agents across multiple tasks, enabling knowledge sh…
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
Daesol Cho, Seungjae Lee, H. Jin Kim
Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics o…
CQM: Curriculum Reinforcement Learning with a Quantized World Model
Seungjae Lee, Daesol Cho, Jonghae Park +1
Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often…