Sensory-Motor Control with Large Language Models via Iterative Policy Refinement
#Large Language Models #Embodied Agents #Policy Refinement #Sensory-Motor Control #Symbolic Knowledge #Gymnasium #MuJoCo #Control Systems
📌 Key Takeaways
- Researchers developed a method enabling LLMs to control physical agents through iterative policy refinement
- The approach combines symbolic reasoning with sensory-motor data for effective control
- The method was successfully validated on classic control tasks using relatively compact language models
- The research represents a significant advancement in AI control systems
📖 Full Retelling
Researchers Jônata Tyska Carvalho and Stefano Nolfi have developed a novel method in February 2026 that enables large language models to control embodied agents through iterative policy refinement, as detailed in their paper accepted for publication in Scientific Reports, addressing the challenge of integrating symbolic reasoning with physical interaction data for effective control systems. The innovative approach allows LLMs to generate control policies that directly map continuous observation vectors to continuous action vectors, enabling more precise control of physical systems. Initially, the language models create a control strategy based on textual descriptions of the agent, its environment, and the intended goals. This strategy then undergoes iterative refinement through a learning process where the LLMs are repeatedly prompted to improve the current strategy using performance feedback and sensory-motor data collected during evaluation. The researchers validated their method on classic control tasks from the Gymnasium library and the inverted pendulum task from the MuJoCo library, with the approach proving effective with relatively compact models such as GPT-oss:120b and Qwen2.5:72b.
🏷️ Themes
Artificial Intelligence, Robotics, Machine Learning
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
🌐
Educational technology
4 shared
🌐
Reinforcement learning
3 shared
🌐
Machine learning
2 shared
🌐
Artificial intelligence
2 shared
🌐
Benchmark
2 shared
Original Source
--> Computer Science > Artificial Intelligence arXiv:2506.04867 [Submitted on 5 Jun 2025 ( v1 ), last revised 24 Feb 2026 (this version, v4)] Title: Sensory-Motor Control with Large Language Models via Iterative Policy Refinement Authors: Jônata Tyska Carvalho , Stefano Nolfi View a PDF of the paper titled Sensory-Motor Control with Large Language Models via Iterative Policy Refinement, by J\^onata Tyska Carvalho and Stefano Nolfi View PDF HTML Abstract: We propose a method that enables large language models to control embodied agents through the generation of control policies that directly map continuous observation vectors to continuous action vectors. At the outset, the LLMs generate a control strategy based on a textual description of the agent, its environment, and the intended goal. This strategy is then iteratively refined through a learning process in which the LLMs are repeatedly prompted to improve the current strategy, using performance feedback and sensory-motor data collected during its evaluation. The method is validated on classic control tasks from the Gymnasium library and the inverted pendulum task from the MuJoCo library. The approach proves effective with relatively compact models such as GPT-oss:120b and Qwen2.5:72b. In most cases, it successfully identifies optimal or near-optimal solutions by integrating symbolic knowledge derived through reasoning with sub-symbolic sensory-motor data gathered as the agent interacts with its environment. Comments: Final version of the article accepted for publication on Scientific Reports. 29 pages (13 pages are from appendix), 8 figures, 2 tables, code for experiments replication and supplementary material provided at this https URL Subjects: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC); Machine Learning (cs.LG cs.RO) Cite as: arXiv:2506.04867 [cs.AI] (or arXiv:2506.04867v4 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2506.04867 Focus to learn more arXiv-issued DOI via Data...
Read full article at source