arXiv:2509. 10317v2 Announce Type: replace-cross Abstract: The article describes the development of a hybrid social robot control architecture to overcome the limitations of traditional approaches, where behavior scripts manually synchronize the robot's actions and text, and existing methods focus primarily on short dialogue responses.
arXiv:2608. 15549v1 Announce Type: cross Abstract: Programming small social robots from natural-language instructions requires more than invoking isolated APIs.
arXiv:2606. 03223v1 Announce Type: cross Abstract: Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways.
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.
arXiv:2607. 22651v1 Announce Type: new Abstract: Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive environments remains a significant challenge.
The paper introduces an LLM chaining architecture for General Purpose Service Robots that splits instruction classification and action generation into two stages, cutting prompt length by about 45% and boosting planning consistency. Evaluation on 100 synthetic GPSR commands across three language models shows consistent improvements over single-prompt methods, with up to +37 percentage points gain on local models. Real‑robot trials on the Toyota HSR confirm that while planning success improves, execution-layer failures remain the main obstacle to full task completion.
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.
arXiv:2607. 22999v1 Announce Type: cross Abstract: Language agents can now interact fluently with users in software, but robots still struggle to bring comparable interaction to physical tasks.
The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.
arXiv:2608. 08160v1 Announce Type: cross Abstract: The rapid advancement of Large Language Models (LLMs) is revolutionizing AI for Games by enabling open-ended and fluid interactive storytelling.
arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.
arXiv:2606. 13722v1 Announce Type: new Abstract: This paper introduces YeasierAgent, an application-building paradigm based on symbiotic agents, narrative worlds, and scene-aware interaction.
The paper introduces an LLM-based Conversational AI Knowledge Assistant for the Raspberry‑Pi‑powered 13‑Axis MyBuddy humanoid robot. It combines large language model-driven language understanding, real‑time speech recognition, internet‑based knowledge retrieval (e.g., Wikipedia, arXiv), flexible dialogue management, and natural speech synthesis to support intelligent, multi‑turn conversations and emotional‑support interactions. This system aims to overcome the limitations of traditional rule‑based dialogue systems in humanoid robots.