Action Sequence Transfer via LLMs for Heterogeneous Environments

Abstract: 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.