5 citations · 5 across the 4 of their papers we have counts for
4 papers
ViReSkill: Vision-Grounded Replanning with Skill Memory for LLM-Based Planning in Lifelong Robot Learning
Tomoyuki Kagaya, Subramanian Lakshmi, Anbang Ye +6
Robots trained via Reinforcement Learning (RL) or Imitation Learning (IL) often adapt slowly to new tasks, whereas recent Large Language Models (LLMs) and Vision-Language Models (V…
Memory Transfer Planning: LLM-driven Context-Aware Code Adaptation for Robot Manipulation
Tomoyuki Kagaya, Subramanian Lakshmi, Yuxuan Lou +6
Large language models (LLMs) are increasingly explored in robot manipulation, but many existing methods struggle to adapt to new environments. Many systems require either environme…
EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI
Tomoyuki Kagaya, Yuxuan Lou, Thong Jing Yuan +8
In recent years, Large Language Models (LLMs) have demonstrated high reasoning capabilities, drawing attention for their applications as agents in various decision-making processes…
RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents
Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou +6
Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming,…