Who's in Charge? Roles and Responsibilities of Decision-Making Components in Conversational Robots

TL;DR

This paper analyzes the organization of decision-making modules in conversational robots, comparing modular and end-to-end architectures, and advocates for balanced architectures with action and interaction managers for improved human-robot interaction.

Contribution

It provides a critical reflection on decision module organization in conversational robots, emphasizing the advantages of balanced architectures over purely modular or end-to-end systems.

Findings

Modular architectures are preferable for complex, collaborative tasks.

Most practical systems are either robot-centric or dialogue-centric.

Balanced architectures with action and interaction managers offer promising future directions.

Abstract

Software architectures for conversational robots typically consist of multiple modules, each designed for a particular processing task or functionality. Some of these modules are developed for the purpose of making decisions about the next action that the robot ought to perform in the current context. Those actions may relate to physical movements, such as driving forward or grasping an object, but may also correspond to communicative acts, such as asking a question to the human user. In this position paper, we reflect on the organization of those decision modules in human-robot interaction platforms. We discuss the relative benefits and limitations of modular vs. end-to-end architectures, and argue that, despite the increasing popularity of end-to-end approaches, modular architectures remain preferable when developing conversational robots designed to execute complex tasks in collaboration with human users. We also show that most practical HRI architectures tend to be either robot-centric or dialogue-centric, depending on where developers wish to place the ``command center'' of their system. While those design choices may be justified in some application domains, they also limit the robot's ability to flexibly interleave physical movements and conversational behaviours. We contend that architectures placing ``action managers'' and ``interaction managers'' on an equal footing may provide the best path forward for future human-robot interaction systems.