Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation

• URL: http://arxiv.org/abs/2303.03480v2
• Date: Sun, 5 Nov 2023 20:49:11 GMT
• Title: Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation
• Authors: Vishnu Sashank Dorbala, James F. Mullen Jr., Dinesh Manocha
• Abstract summary: We present LGX, a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON) Our approach makes use of Large Language Models (LLMs) for this task. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline.
• Score: 58.3480730643517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present LGX (Language-guided Exploration), a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON), where an embodied agent navigates to a 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 commonsense reasoning capabilities for making sequential navigational decisions. Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of 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 present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via \textit{real-world} experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.

Related papers

• SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation [83.4599149936183]
  Existing zero-shot object navigation methods prompt LLM with the text of spatially closed objects. We propose to represent the observed scene with 3D scene graph. We conduct extensive experiments on MP3D, HM3D and RoboTHOR environments, where SG-Nav surpasses previous state-of-the-art zero-shot methods by more than 10% SR on all benchmarks.
  arXiv  Detail & Related papers  (2024-10-10T17:57:19Z)
• LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation [41.34703238334362]
  LOC-ZSON is a novel Language-driven Object-Centric image representation for object navigation task within complex scenes. We propose an object-centric image representation and corresponding losses for visual-language model (VLM) fine-tuning. We implement our method on Astro robot and deploy it in both simulated and real-world environments for zero-shot object navigation.
  arXiv  Detail & Related papers  (2024-05-08T18:45:37Z)
• GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation [65.71524410114797]
  GOAT-Bench is a benchmark for the universal navigation task GO to AnyThing (GOAT) In GOAT, the agent is directed to navigate to a sequence of targets specified by the category name, language description, or image. We benchmark monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities.
  arXiv  Detail & Related papers  (2024-04-09T20:40:00Z)
• Cognitive Planning for Object Goal Navigation using Generative AI Models [0.979851640406258]
  We present a novel framework for solving the object goal navigation problem that generates efficient exploration strategies. Our approach enables a robot to navigate unfamiliar environments by leveraging Large Language Models (LLMs) and Large Vision-Language Models (LVLMs)
  arXiv  Detail & Related papers  (2024-03-30T10:54:59Z)
• OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models [16.50443396055173]
  We propose OpenFMNav, an Open-set Foundation Model based framework for zero-shot object navigation. We first unleash the reasoning abilities of large language models to extract proposed objects from natural language instructions. We then leverage the generalizability of large vision language models to actively discover and detect candidate objects from the scene.
  arXiv  Detail & Related papers  (2024-02-16T13:21:33Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models [17.495162643127003]
  We introduce the NavGPT to reveal the reasoning capability of GPT models in complex embodied scenes. NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status. We show that NavGPT is capable of generating high-quality navigational instructions from observations and actions along a path.
  arXiv  Detail & Related papers  (2023-05-26T14:41:06Z)
• ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation [75.13546386761153]
  We present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC) ESC transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience. Experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines.
  arXiv  Detail & Related papers  (2023-01-30T18:37:32Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.