Model-Based AI planning and Execution Systems for Robotics (2505.04493v1)

Abstract: Model-based planning and execution systems offer a principled approach to building flexible autonomous robots that can perform diverse tasks by automatically combining a host of basic skills. This idea is almost as old as modern robotics. Yet, while diverse general-purpose reasoning architectures have been proposed since, general-purpose systems that are integrated with modern robotic platforms have emerged only recently, starting with the influential ROSPlan system. Since then, a growing number of model-based systems for robot task-level control have emerged. In this paper, we consider the diverse design choices and issues existing systems attempt to address, the different solutions proposed so far, and suggest avenues for future development.

Model-Based AI Planning and Execution Systems for Robotics: A Substantive Overview

The paper "Model-Based AI Planning and Execution Systems for Robotics" by Or Wertheim and Ronen I. Brafman presents a comprehensive examination of model-based planning and execution systems for robotics (MPER). These systems harness AI-based planning techniques for task-level control in autonomous robots, integrating a diverse set of core skills such as navigation, localization, and object manipulation. The focus is on adapting to dynamic environments through the use of sophisticated software architectures.

Design Challenges and Solutions

The authors outline several design challenges in MPER development:

1. Modeling: Systems must effectively model robot skills, tasks, and environmental states. Varying levels of uncertainty—probabilistic and non-deterministic—are considered in defining these models.
2. Decision Making: MPERs use both online and offline planning strategies. The paper highlights the importance of replanning to address unexpected state changes, the use of goal reasoning, and model simplifications for computational feasibilities.
3. State Representation and Update: Maintaining dynamic world states and execution states, via AI planners, is critical. Systems often balance between maintaining rich state information for comprehensive decision-making and containing processing overhead for efficiency.
4. Integration: Integrating diverse skill code bases into MPERs without excessively increasing complexity is crucial. The ease of integration can affect system usability across different robotic platforms and contexts.

System Architecture and Impact

Key systems like ROSPlan, CLIPS Executive, PlanSys2, and SkiROS2 are explored, each characterized by its approach to modeling, decision-making, integration, and impact on robotic research. ROSPlan emerges for its extensive planner support and model use, while CLIPS Executive distinguishes itself with its rule-based expert system for flexibility in goal reasoning. Meanwhile, PlanSys2 and SkiROS2 emphasize improvements in usability, ROS2 support, and sophisticated monitoring interfaces.

Strengths and Limitations

Strong empirical demonstrations, such as the deployment in RoboCup challenges by systems like the CLIPS Executive, underscore the practical viability of MPERs. However, the complexity of formal modeling and integration poses challenges. The paper suggests potential solutions like leveraging open-model communities or automating model generation, hinting at opportunities for further reducing barriers to widespread use.

Future Research Directions

The authors propose multiple avenues for future MPER research:

• Multi-level Hybrid Design: MPERs need to incorporate both deliberative and reactive planning components, enabling complex skill executions through hierarchical decompositions involving high-level plans and solver activations.
• Enhanced Sensing and Monitoring: Improvements in defining and executing monitoring processes, combined with automated safety checks, could significantly enhance system robustness and reliability.
• Human Control Interfaces: Developing comprehensive interfaces for plan and state management could substantially aid in deploying MPERs in practical, user-critical settings.

Implications and Prospective Developments

As AI and machine learning methodologies advance, especially with LLMs, MPERs are poised to utilize these capabilities to streamline model creation, clarify decision-making processes, and simplify user interaction. Importantly, addressing regulatory concerns through transparent and verifiable skill models positions MPERs as pivotal in the ongoing evolution of autonomous robotic systems.

In conclusion, Wertheim and Brafman's work articulates a clear snapshot of the model-based planning and execution landscape in robotics. Their synthesis of current challenges and future directions lays a foundation for subsequent innovations that can drive both theoretical and applied advancements in AI-enabled robotics.