- The paper introduces a comprehensive pipeline that combines high-level planning with reinforcement learning to improve robotic execution in adaptive manufacturing.
- It demonstrates SkiROS2’s integration of ontologies, PDDL-based task management, and behavior trees to support automation in semi-structured workspaces.
- The study employs multi-objective reinforcement learning with parameter priors and supervised learning to address task variability and optimize performance.
Flexible and Adaptive Manufacturing by Integrating Knowledge Representation, Reasoning, Planning, and Reinforcement Learning
The paper investigates the integration of knowledge representation, reasoning, and task-level planning with reinforcement learning (RL) to advance flexible and adaptive manufacturing systems. This paper is situated within the context of evolving manufacturing needs, transitioning from mass production to enhanced customization and smaller batch sizes. Such a transition necessitates systems that can effectively manage increased complexity associated with robotic system integration. The research introduces a comprehensive pipeline that combines high-level planning goals with RL to achieve tangible execution improvements in robotic systems.
SkiROS2 Skill-Based System
At the core of this research is SkiROS2, a skill-based robot control system built on the Robot Operating System (ROS). SkiROS2 is designed for semi-structured workspaces, providing tools to manage the dynamism inherent in these environments. The kernel of SkiROS2 is its world model, structured upon an RDF graph for robust knowledge representation. The system incorporates ontologies to define relevant concepts, properties, and relations, with skill managers retrieving and executing skills based on a semantic layer. SkiROS2 thus offers a platform for integrating diverse solutions, from motion planning to visual recognition, with the capability to adapt skills dynamically based on environmental changes.
Planning, Knowledge Representation, and Reasoning
SkiROS2's architecture supports flexible manufacturing by leveraging a sophisticated task manager capable of handling goals in PDDL and automatically generating domain and problem descriptions. This separation of task knowledge from skill implementation enhances adaptability across different robot hardware and tasks. The architecture supports spatial reasoning and task parameterization, further extending its capabilities through the use of behavior trees and motion generators (BTMG) for skill implementation.
Reinforcement Learning and Multi-objective Learning
A significant contribution of the paper is the integration of RL into the planning and reasoning infrastructure. This approach facilitates learning contact-rich tasks with compliant control—critical for tasks directly on the real system or those transferred from simulation. The use of multi-objective RL acknowledges the presence of multiple performance indicators, enabling the operator to choose from policies along the Pareto frontier. This approach not only eases the learning scenario design but also optimizes performance across various objectives. Furthermore, the implementation of parameter priors helps expedite the search for optimal solutions by leveraging operator input or pre-existing experience.
Learning for Task Variations
The challenge of adapting to task variations is addressed by framing it as a supervised learning problem. The paper proposes a model that integrates BTMG parameters for random task variations. Combining Gaussian Processes and weighted support vector machine classifiers, this model learns reward and feasibility functions, optimizing the reward while ensuring performing task completion. The integration of planning and reasoning with model inference provides comprehensive parameterization, demonstrating effectiveness in addressing unseen task variations.
Conclusions
The implications of this research are substantial for contemporary manufacturing systems. By merging task-level planning, knowledge representation, and RL, the authors present a method that supports task adaptation while enhancing the ability of robotic systems to learn and improve task execution. The multi-objective learning formulation simplifies the problem definition and supports operator-driven policy selection. Additionally, integration of user-driven priors into the learning process accelerates learning and enhances safety. The capability to adapt to task variations through machine learning models further positions these techniques for employment in future manufacturing environments. This work sets a foundation for ongoing exploration and development within intelligent manufacturing systems.