arXiv:2610.18874v1 Announce Type: cross
Abstract: Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-actio...
By Bingxin Xu, Wenrui Bao, Xinxin Liu, Jiawei Zhou, Yuzhang Shang, Emilio Ferrara
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and th...
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu
PIVOT (Plan-Inspect-eVOlve Trajectories) is a self‑supervised framework that refines large‑language‑model agent plans by treating trajectories as optimizable objects. It operates in four stages—PLAN, INSPECT, EVOLVE, and VERIFY—using structured losses and textual gradients to iteratively improve trajectories and ensure compliance with task constraints. Experiments on DeepPlanning and GAIA show that PIVOT achieves up to 94% relative improvement in constraint satisfaction with human‑in‑the‑loop feedback and still delivers significant gains autonomously, while using 3x to 5x fewer tokens than competing methods.
By Tuo Zhang, Alin-Ionut Popa, Yan Xu, Rui Song, Dimitrios Dimitriadis
arXiv:2609.22813v1 Announce Type: cross
Abstract: We present \emph{commonsense ranked search} (CoRS), a novel path planner that turns an abstract instruction into a route that follows commonsense. Wh...
By Masafumi Endo, Kohei Honda, Ryo Yonetani
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
By Jorge Mendez-Mendez