Exploring Unseen Environments with Robots using Large Language and Vision Models through a Procedurally Generated 3D Scene Representation

• URL: http://arxiv.org/abs/2404.00318v1
• Date: Sat, 30 Mar 2024 10:54:59 GMT
• Title: Exploring Unseen Environments with Robots using Large Language and Vision Models through a Procedurally Generated 3D Scene Representation
• Authors: Arjun P S, Andrew Melnik, Gora Chand Nandi,
• Abstract summary: This work focuses on solving the object goal navigation problem by mimicking human cognition. We introduce a comprehensive framework capable of exploring an unfamiliar environment in search of an object. A challenging task in using LLMs to generate high level sub-goals is to efficiently represent the environment around the robot.
• Score: 0.979851640406258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Generative Artificial Intelligence, particularly in the realm of Large Language Models (LLMs) and Large Vision Language Models (LVLMs), have enabled the prospect of leveraging cognitive planners within robotic systems. This work focuses on solving the object goal navigation problem by mimicking human cognition to attend, perceive and store task specific information and generate plans with the same. We introduce a comprehensive framework capable of exploring an unfamiliar environment in search of an object by leveraging the capabilities of Large Language Models(LLMs) and Large Vision Language Models (LVLMs) in understanding the underlying semantics of our world. A challenging task in using LLMs to generate high level sub-goals is to efficiently represent the environment around the robot. We propose to use a 3D scene modular representation, with semantically rich descriptions of the object, to provide the LLM with task relevant information. But providing the LLM with a mass of contextual information (rich 3D scene semantic representation), can lead to redundant and inefficient plans. We propose to use an LLM based pruner that leverages the capabilities of in-context learning to prune out irrelevant goal specific information.

Related papers

• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  Large Language Models (LLMs) equipped with extensive world knowledge and strong reasoning skills can tackle diverse tasks across domains. We propose LLaRA: Large Language and Robotics Assistant, a framework which formulates robot action policy as conversations.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models [113.18524940863841]
  This survey provides a comprehensive overview of the methodologies enabling large language models to process, understand, and generate 3D data. Our investigation spans various 3D data representations, from point clouds to Neural Radiance Fields (NeRFs) It examines their integration with LLMs for tasks such as 3D scene understanding, captioning, question-answering, and dialogue.
  arXiv  Detail & Related papers  (2024-05-16T16:59:58Z)
• MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting [106.53784213239479]
  We present MOKA (Marking Open-vocabulary Keypoint Affordances), an approach that employs vision language models to solve robotic manipulation tasks. At the heart of our approach is a compact point-based representation of affordance and motion that bridges the VLM's predictions on RGB images and the robot's motions in the physical world. We evaluate and analyze MOKA's performance on a variety of manipulation tasks specified by free-form language descriptions.
  arXiv  Detail & Related papers  (2024-03-05T18:08:45Z)
• LLMs for Robotic Object Disambiguation [21.101902684740796]
  Our study reveals the LLM's aptitude for solving complex decision making challenges. A pivotal focus of our research is the object disambiguation capability of LLMs. We have developed a few-shot prompt engineering system to improve the LLM's ability to pose disambiguating queries.
  arXiv  Detail & Related papers  (2024-01-07T04:46:23Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning [32.045840007623276]
  We introduce Robotic Vision-Language Planning (ViLa), a novel approach for long-horizon robotic planning. ViLa directly integrates perceptual data into its reasoning and planning process. Our evaluation, conducted in both real-robot and simulated environments, demonstrates ViLa's superiority over existing LLM-based planners.
  arXiv  Detail & Related papers  (2023-11-29T17:46:25Z)
• An Embodied Generalist Agent in 3D World [67.16935110789528]
  We introduce LEO, an embodied multi-modal generalist agent that excels in perceiving, grounding, reasoning, planning, and acting in the 3D world. We collect large-scale datasets comprising diverse object-level and scene-level tasks, which require considerable understanding of and interaction with the 3D world. Through extensive experiments, we demonstrate LEO's remarkable proficiency across a wide spectrum of tasks, including 3D captioning, question answering, embodied reasoning, navigation and manipulation.
  arXiv  Detail & Related papers  (2023-11-18T01:21:38Z)
• LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent [23.134180979449823]
  3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. We propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries.
  arXiv  Detail & Related papers  (2023-09-21T17:59:45Z)
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models [38.503337052122234]
  Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation. We aim to synthesize robot trajectories for a variety of manipulation tasks given an open-set of instructions and an open-set of objects. We demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions.
  arXiv  Detail & Related papers  (2023-07-12T07:40:48Z)
• LanguageRefer: Spatial-Language Model for 3D Visual Grounding [72.7618059299306]
  We develop a spatial-language model for a 3D visual grounding problem. We show that our model performs competitively on visio-linguistic datasets proposed by ReferIt3D.
  arXiv  Detail & Related papers  (2021-07-07T18:55:03Z)

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.