Robot planning via LLM proposals and symbolic verification

Pesjak, Drejc (avtor), Žabkar, Jure (avtor)

Planning in robotics represents an ongoing research challenge, as it requires the integration of sensing, reasoning, and execution. Although large language models (LLMs) provide a high degree of flexibility in planning, they often introduce hallucinated goals and actions and consequently lack the formal reliability of deterministic methods. In this paper, we address this limitation by proposing a hybrid Sense–Plan–Code–Act (SPCA) framework that combines perception, LLM-based reasoning, and symbolic planning. Within the proposed approach, sensory information is first transformed into a symbolic description of the world in Planning Domain Definition Language (PDDL) using an LLM. A heuristic planner is then used to generate a valid plan, which is subsequently converted to code by a second LLM. The generated code is first validated syntactically through compilation and then semantically in simulation. When errors are detected, local corrections can be applied and the process is repeated as necessary. The proposed method is evaluated in the OpenAI Gym MiniGrid reinforcement learning environment and in a Gazebo simulation on a UR5 robotic arm using a curriculum of tasks with increasing complexity. The system successfully completes approximately 71–75% of tasks across environments with a relatively low number of simulation iterations.

• Jezik: Angleški jezik
• Ključne besede: deep reinforcement learning, explainability, prototypes, large language models (LLMs), symbolic planning, planning domain definition language (PDDL), program synthesis, embodied agents, robotic manipulation
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 36 str.
• Številčenje: Vol. 8, iss. 1, art. 22
• PID: 20.500.12556/RUL-178669
• UDK: 004.8:007.52
• ISSN pri članku: 2504-4990
• DOI: 10.3390/make8010022
• COBISS.SI-ID: 265132291
• Datum objave v RUL: 29.01.2026

Gradivo je del revije

• Naslov: Machine learning and knowledge extraction
• Založnik: MDPI
• ISSN: 2504-4990
• COBISS.SI-ID: 1537706179

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: globoko spodbujevano učenje, razložljivost, prototipi