LLMによる異種環境間の行動系列転移
Action Sequence Transfer via LLMs for Heterogeneous Environments
ユーザーの行動系列を、シーングラフ表現を用いて異なる環境の物体配置に適応させて転移するシステムを提案し、Ego4D GoalStepから構築したデータセットで有効性を示した。
著者: Choongho Chung, DongHwan Shin, Sung-Hee Lee
分類: cs.RO
原文アブストラクト
We present an action sequence transfer system that adaptively transfers user action sequences across different target spaces. Given an input action sequence from a source space and scene graph representations of both the source and target environments, our system predicts a corresponding action sequence in the target space by adapting to the spatial and object constraints of the new environment. To achieve this, we leverage multi-level representations of user activity to generalize actions at varying levels of abstraction. To demonstrate our system, we collect a new scene graph-based dataset derived from the Ego4D GoalStep dataset for evaluation. Results indicate that our system can generate valid action sequences even between spaces with drastically different object configurations.