1 citations · 1 across the 7 of their papers we have counts for
7 papers
Action- and Language-Conditioned Video Assessment for Embodied Control
Hwanhee Kim, Jaehyun Jang, Seungmin Cha +3
Vision-based embodied agents executing multi-step natural language instructions require feedback mechanisms that assess task progress over complete trajectories. Conventional appro…
Video-Based Optimal Transport for Feedback-Efficient Offline Preference-Based Reinforcement Learning
Tung M. Luu, Hwanhee Kim, Younghwan Lee +1
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL (PbRL) offers a promising alternative by learni…
Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications
Chanwoo Kim, Jihwan Yoon, Hyeonseong Kim +9
Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that in…
Policy Learning from Large Vision-Language Model Feedback without Reward Modeling
Tung M. Luu, Donghoon Lee, Younghwan Lee +1
Offline reinforcement learning (RL) provides a powerful framework for training robotic agents using pre-collected, suboptimal datasets, eliminating the need for costly, time-consum…
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models
Tung Minh Luu, Younghwan Lee, Donghoon Lee +3
Designing effective reward functions remains a fundamental challenge in reinforcement learning (RL), as it often requires extensive human effort and domain expertise. While RL from…
Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation
Donghoon Lee, Tung M. Luu, Younghwan Lee +1
Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment…
Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning
Younghwan Lee, Tung M. Luu, Donghoon Lee +1
In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risk…