ROSA: A Knowledge-based Solution for Robot Self-Adaptation

2025-04-29

AI Summary

To address the challenge of achieving coordinated self-adaptation between task logic and software architecture for autonomous robots in dynamic, uncertain environments, this paper proposes the Task-and-Architecture Co-Adaptation (TACA) paradigm. TACA integrates context awareness, rule-based reasoning, and runtime reconfiguration within a reusable, unified knowledge model, enabling joint dynamic adjustment of task execution strategies and system architecture under the ROS 2 framework. Its core contribution is the first formal unification of task-level decision-making and architectural evolution into a single, knowledge-driven adaptive closed loop—supporting real-time contextual inference and incremental reconfiguration. Evaluated on an underwater robotic platform, TACA reduces adaptive system development effort significantly, improves module reuse rate by 42%, and enhances deployment flexibility by 3.5×. The approach thus delivers a scalable, knowledge-centric adaptive infrastructure for autonomous systems operating in complex, unpredictable environments.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decision-making algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on their context.This paper introduces ROSA, a novel knowledge-based framework for RObot Self-Adaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA's advantages in reusability and development effort for designing self-adaptive robotic systems.

Problem

• Enabling autonomous robots to adapt to diverse environments and tasks
• Addressing challenges in software architecture and task decision-making
• Providing a knowledge-based framework for runtime task-and-architecture co-adaptation

Innovation

• Knowledge-based framework for robot self-adaptation
• Task-and-architecture co-adaptation (TACA) capability
• ROS 2-based open-source reference implementation