12 citations · 36 across the 24 of their papers we have counts for
5 papers · 1 filter
PHUMA: Physically Reliable Humanoid Locomotion Dataset
Kyungmin Lee, Sibeen Kim, Youngdo Lee +6
Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture dat…
ACG: Action Coherence Guidance for Flow-based Vision-Language-Action models
Minho Park, Kinam Kim, Junha Hyung +5
Diffusion and flow matching models have emerged as powerful robot policies, enabling Vision-Language-Action (VLA) models to generalize across diverse scenes and instructions. Yet,…
Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
Dongyoon Hwang, Hojoon Lee, Jaegul Choo +2
While reinforcement learning (RL) for large language models (LLMs) has shown promise in mathematical reasoning, strategic reasoning for LLMs using RL remains largely unexplored. We…
A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
Hojoon Lee, Takuma Seno, Jun Jet Tai +4
Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require ins…
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee, Youngdo Lee, Takuma Seno +3
Scaling up the model size and computation has brought consistent performance improvements in supervised learning. However, this lesson often fails to apply to reinforcement learnin…