arXiv:2608. 15549v1 Announce Type: cross Abstract: Programming small social robots from natural-language instructions requires more than invoking isolated APIs.
By Xiao Wang, Lu Dong, Ifeoma Nwogu, Srirangaraj Setlur, Venu Govindaraju
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.
By Zhe Sun, Meng Wang, Lei Wang, Yuxi Wang, Wanxin Li, Yujia Peng, Zhenliang Zhang
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.
By Kazi Abrar Mahmud, Nilotpaul Kundu Dhurubo, Tamal Kirttonia, Sabbir Hossain Ujjal, Mohammad Ariful Haque
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.
By Luka Borozan, Domagoj Matijevi\'c
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.
By Lucas Da Mota Bruno, Jiahao Sim, Yoshinobu Hagiwara
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.
By Guilhem Fouilh\'e, Rebecca Eifler, Antonin Poch\'e, Sylvie Thi\'ebaux, Nicholas Asher