1 citations · 1 across the 1 of their papers we have counts for
5 papers
AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning
Yangzhe Kong, Daeun Song, Jing Liang +3
We present a novel method, AutoSpatial, an efficient approach with structured spatial grounding to enhance VLMs' spatial reasoning. By combining minimal manual supervision with lar…
Evaluating Vision-Language Models as Evaluators in Path Planning
Mohamed Aghzal, Xiang Yue, Erion Plaku +1
Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intrigui…
Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning
Mohamed Aghzal, Erion Plaku, Ziyu Yao
Large language models (LLMs) have achieved remarkable success across a wide spectrum of tasks; however, they still face limitations in scenarios that demand long-term planning and…
A Survey on Large Language Models for Automated Planning
Mohamed Aghzal, Erion Plaku, Gregory J. Stein +1
The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability t…
Look Further Ahead: Testing the Limits of GPT-4 in Path Planning
Mohamed Aghzal, Erion Plaku, Ziyu Yao
Large Language Models (LLMs) have shown impressive capabilities across a wide variety of tasks. However, they still face challenges with long-horizon planning. To study this, we pr…