Papers by Honguk Woo
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble (2024.findings-emnlp)
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| Challenge: | Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. |
| Approach: | They propose a consistency-guided reward ensemble framework to train agents offline via offline reinforcement learning (RL) they use spatio-temporally consistent rewards to derive domain-grounded rewards from training datasets. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based agents with 8B parameters and has 117M parameters for agent policy network and only for training. |
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments (2024.findings-acl)
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| Challenge: | Existing frameworks for grounding pretrained language models as task planners are challenging due to their intricate entanglement with domain knowledge. |
| Approach: | They propose a framework that leverages the hierarchical nature of semantic skills to ground them in different domains. |
| Outcome: | The proposed framework is effective in 300 cross-domain EIF scenarios. |