This paper addresses a challenging interactive task learning scenario we call rearrangement under unawareness: to manipulate a rigid-body environment in a context where the agent is unaware of a concept that is key to solving the instructed task. We propose SECURE, an interactive task learning framework designed to solve such problems. It uses embodied conversation to fix its deficient domain model -- through dialogue, the agent discovers and then learns to exploit unforeseen possibilities. In particular, SECURE learns from the user's embodied corrective feedback when it makes a mistake, and it makes strategic dialogue decisions to reveal useful evidence about novel concepts for solving the instructed task. Together, these abilities allow the agent to generalise to subsequent tasks using newly acquired knowledge. We demonstrate that learning to solve rearrangement under unawareness is more data efficient when the agent is semantics-aware -- that is, during both learning and inference it augments the evidence from the user's embodied conversation with its logical consequences, stemming from semantic analysis.
View on arXiv@article{rubavicius2025_2409.17755,
title={ SECURE: Semantics-aware Embodied Conversation under Unawareness for Lifelong Robot Learning },
author={ Rimvydas Rubavicius and Peter David Fagan and Alex Lascarides and Subramanian Ramamoorthy },
journal={arXiv preprint arXiv:2409.17755},
year={ 2025 }
}