While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions. To address this limitation, this manuscript introduces an agentic MPC framework that enables context-aware, semantically adaptive control synthesis by integrating with large language model-based agents.
arXiv:2606. 12774v1 Announce Type: cross Abstract: While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions.
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.
arXiv:2608. 20129v1 Announce Type: cross Abstract: Autonomous vehicles require robust perception and decision-making capabilities to operate in diverse and unseen scenarios.
arXiv:2601.12538v2 Announce Type: replace-cross Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) d...
arXiv:2607. 00989v1 Announce Type: cross Abstract: Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
The paper introduces AI Control Scientist (AICS), a large language model–driven agent that automatically generates optimized controllers from language design requirements. AICS consists of a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show that AICS outperforms existing automated baselines in design success rate and optimization efficiency, demonstrating its potential to shift control system design from human‑driven to agent‑driven approaches.
arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.
AI Control Scientist (AICS) is a large language model–driven agent that automatically generates optimized controllers from language design requirements. It comprises a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show AICS outperforms existing automated baselines in design success rate and optimization efficiency, enabling the creation of multiple representative control systems.
arXiv:2608. 03018v1 Announce Type: new Abstract: Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet.
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
The paper introduces a ROS-Agent architecture that enhances task reliability and execution efficiency for open‑source LLM‑powered robotic agents. It adds a MetaTool that forces the LLM to produce a structured pseudo‑code plan before any action, storing this plan in a scratchpad to separate planning from execution. Experiments on a custom mobile robot show up to ~24% improvement in complex task completion and contextual consistency compared to the baseline.