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Ze Ji

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Conference Aug 2026

Cost-Aware LLM-Based Task and Motion Planning with Learned Feasibility Checks

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 · 0 citations
Conference Aug 2026

CardiffNav: A GNSS-centric Multisensory Dataset for Robust Localisation in Diverse and Challenging Environments

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. · 0 citations
Conference Aug 2026

Prompted to Explore: Training-Time-Only LLM Proposals for Sample-Efficient Pushing Grasping Policies

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 · 0 citations

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