HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams (2409.18047v2)

Abstract: This paper introduces HARMONIC, a cognitive-robotic architecture that integrates the OntoAgent cognitive framework with general-purpose robot control systems applied to human-robot teaming (HRT). We also present a cognitive strategy for robots that incorporates metacognition, natural language communication, and explainability capabilities required for collaborative partnerships in HRT. Through simulation experiments involving a joint search task performed by a heterogeneous team of a UGV, a drone, and a human operator, we demonstrate the system's ability to coordinate actions between robots with heterogeneous capabilities, adapt to complex scenarios, and facilitate natural human-robot communication. Evaluation results show that robots using the OntoAgent architecture within the HARMONIC framework can reason about plans, goals, and team member attitudes while providing clear explanations for their decisions, which are essential prerequisites for realistic human-robot teaming.

• The paper introduces the HARMONIC architecture, which combines strategic cognitive processing with tactical control using behavior trees to enhance team coordination.
• It employs natural language processing and OntoAgent-based decision-making to facilitate effective communication and dynamic role assignment in heterogeneous teams.
• The study demonstrates robust performance in simulated search tasks and outlines future directions for real-world applications and adaptive learning.

An Expert Overview of "HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams"

The paper "HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams" presents a sophisticated approach to human-robot teaming, emphasizing the integration of cognitive strategies with robotic control systems. The HARMONIC architecture is a key contribution of this work, characterized by its dual-layer design that combines strategic cognitive processing with tactical robot control, thereby optimizing functionality in collaborative multi-agent environments.

Core Contributions and Methodological Approach

The framework outlined in the paper addresses several challenges inherent in human-robot teaming, particularly in heterogeneous groups where entities must communicate and collaborate effectively. The HARMONIC architecture is structured into two primary layers:

1. Strategic Cognitive Layer: This layer is based on the OntoAgent cognitive architecture, which is content-centric and supports decision-making processes that require complex reasoning, metacognition, and language understanding. OntoAgent's design facilitates actions within human-robot teams through modules for attention management and perception interpretation. It emphasizes natural language processing for effective communication and uses a comprehensive knowledge base, OntoGraph, to ensure seamless semantic integration across modalities.
2. Tactical Control Layer: Employing Behavior Trees (BTs), this layer manages the execution of robot actions, reacting to dynamic environmental changes and controlling lower-level operational tasks. BTs are particularly advantageous due to their scalability and modularity, making them suitable for representing complex action plans and control tasks that are dynamically adaptable.

The collaboration demonstrated in this paper involves a human, a drone, and a UGV executing a search task in a simulated apartment environment. The human initializes the task, and the UGV, as the task leader, navigates preconditions for the search operation by interacting with the human. The agents utilize natural language communication to facilitate task coordination and implementation, showcasing a nuanced understanding of role-based task execution and dynamic plan adaptation.

Implications and Future Directions

The HARMONIC framework offers significant advancements in the field of human-robot interaction by providing a transparent, explainable architecture that can foster trust in robotic systems. Its dual-layer approach effectively mimics human cognitive processes, thereby supporting complex collaborative and communication tasks. The strategic use of natural language processing and explainability in action selection and reasoning enhances human-robot team dynamics, adhering to the growing demand for trustworthy AI systems.

The paper speculates on potential future developments, emphasizing the need to expand the range of tasks and scenarios for HARMONIC agents. Enhancements in learning capabilities and real-world trials are proposed to validate this model's effectiveness in broader applications. Moreover, exploring dynamic team hierarchies and collaborative online planning are recognized as pivotal steps for future research, particularly in optimizing leadership and control strategies in diverse team structures.

In summary, the HARMONIC architecture represents a significant step forward in realizing effective human-robot teaming through its integration of cognitive metacognitive strategies within robotic operations. Its ability to engage in meaningful language interaction and reason about complex tasks makes it a compelling model for advancing AI's role in multi-agent systems. The framework's transparency and focus on explainability are poised to greatly contribute to the development of reliable and interpretable AI technologies in the robotics domain.