Large Language Models (LLMs) have emerged as powerful zero-shot task planners for robotic manipulation, reducing the need for manually specified symbolic models in classical Task and Motion Planning (TAMP). However, in closed-loop LLM–planner systems, motion planning (MP) is often the primary source of feasibility feed...
Mei-Yuan Gong, Yan Gao, Ze Ji· International Conference on...· 0 citations
Robust state estimation via multi-sensor fusion is strictly constrained in urban environments, driven primarily by Non-Line-of-Sight (NLOS) and multipath interference acting upon Global Navigation Satellite System (GNSS) signals. Current benchmark datasets systematically omit the raw GNSS observables requisite for form...
Shun Liu, Yixin Chen, R. Pullin et al.· 2026 IEEE International Conf...· 0 citations
Learning long-horizon robot manipulation remains difficult and time-consuming, especially under sparse rewards due to inefficient exploration and reward assignment. We present a minimal integration of large language models (LLMs) with reinforcement learning (RL) in which the LLM is used strictly as an online action pro...
Meiyuan Gong, Yan Gao, Ze Ji· 2026 IEEE International Conf...· 0 citations
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