CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning
arXiv:2406.03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. However, it is challenging to ground a LLM-generated plan to be executable for the specified robot with certain restrictions. This paper introduces CLMASP, an approach that couples LLMs with Answer Set Programming (ASP) to overcome the limitations, where ASP is a non-monotonic logic programming formalism renowned for its capacity to represent and reason about a robot's
The rapid advancement of large language models combined with the increasing demand for robust and adaptable robotic systems is driving innovation in integrating these technologies.
This development represents a significant step towards enabling more autonomous and versatile robots capable of performing complex tasks in real-world, open-ended environments.
The ability to ground LLM-generated plans with formal logic programming will allow robots to execute tasks with higher reliability and adherence to physical constraints, moving beyond purely theoretical or simulated environments.
- · Robotics companies
- · AI software developers
- · Logistics and manufacturing sectors
- · Defence contractors
- · Manual task-oriented labor
- · Companies relying on less autonomous robotic solutions
Enhanced robotic capabilities across various industries, from manufacturing to exploration, will emerge.
The demand for skilled engineers capable of integrating and managing complex AI-driven robotic systems will increase significantly.
This could accelerate the deployment of autonomous systems into sensitive or hazardous environments, potentially reshaping geopolitical strategies related to remote operations and resource extraction.
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Read at arXiv cs.AI