首页|New Robotics and Automation Findings from University of Maryland Outlined (can a n Embodied Agent Find Your 'cat-shaped Mug'? Llm-based Zero-shot Object Navigati on)
New Robotics and Automation Findings from University of Maryland Outlined (can a n Embodied Agent Find Your 'cat-shaped Mug'? Llm-based Zero-shot Object Navigati on)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news originating from Col lege Park, Maryland, by NewsRx correspondents, research stated, “We present lang uage-guided exploration (LGX), a novel algorithm for Language-Driven Zero-Shot O bject Goal Navigation (L-ZSON), where an embodied agent navigates to an uniquely described target object in a previously unseen environment. Our approach makes use of large language models (LLMs) for this task by leveraging the LLM’s common sense-reasoning capabilities for making sequential navigational decisions.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of M aryland, “Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-s hot object navigation results on RoboTHOR with a success rate (SR) improvement o f over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and pre sent an analysis of various prompting strategies affecting the model output.”
College ParkMarylandUnited StatesN orth and Central AmericaRobotics and AutomationRoboticsUniversity of Maryl and
NETL
NSTL
