TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models

• URL: http://arxiv.org/abs/2412.15462v1
• Date: Thu, 19 Dec 2024 23:43:40 GMT
• Title: TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models
• Authors: Ammar N. Abbas, Csaba Beleznai,
• Abstract summary: The presented paper investigates recent advancements in Large Language Models (LLMs) and Vision Language Models (VLMs) This integration allows robots to understand and execute commands given in natural language and to perceive their environment through visual and/or descriptive inputs. Our paper outlines four LLM-assisted simulated robotic control, which explore (i) low-level control, (ii) the generation of language-based feedback that describes the robot's internal states, (iii) the use of visual information as additional input, and (iv) the use of robot structure information for generating task plans and feedback.
• Score: 1.534667887016089
• License:
• Abstract: TalkWithMachines aims to enhance human-robot interaction by contributing to interpretable industrial robotic systems, especially for safety-critical applications. The presented paper investigates recent advancements in Large Language Models (LLMs) and Vision Language Models (VLMs), in combination with robotic perception and control. This integration allows robots to understand and execute commands given in natural language and to perceive their environment through visual and/or descriptive inputs. Moreover, translating the LLM's internal states and reasoning into text that humans can easily understand ensures that operators gain a clearer insight into the robot's current state and intentions, which is essential for effective and safe operation. Our paper outlines four LLM-assisted simulated robotic control workflows, which explore (i) low-level control, (ii) the generation of language-based feedback that describes the robot's internal states, (iii) the use of visual information as additional input, and (iv) the use of robot structure information for generating task plans and feedback, taking the robot's physical capabilities and limitations into account. The proposed concepts are presented in a set of experiments, along with a brief discussion. Project description, videos, and supplementary materials will be available on the project website: https://talk-machines.github.io.

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations. First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets. We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Large Language Models for Robotics: Opportunities, Challenges, and Perspectives [46.57277568357048]
  Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. For embodied tasks, where robots interact with complex environments, text-only LLMs often face challenges due to a lack of compatibility with robotic visual perception. We propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.
  arXiv  Detail & Related papers  (2024-01-09T03:22:16Z)
• QUAR-VLA: Vision-Language-Action Model for Quadruped Robots [37.952398683031895]
  The central idea is to elevate the overall intelligence of the robot. We propose QUAdruped Robotic Transformer (QUART), a family of VLA models to integrate visual information and instructions from diverse modalities as input. Our approach leads to performant robotic policies and enables QUART to obtain a range of emergent capabilities.
  arXiv  Detail & Related papers  (2023-12-22T06:15:03Z)
• 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)
• Large Language Models for Robotics: A Survey [40.76581696885846]
  Large language models (LLMs) possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. This review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning.
  arXiv  Detail & Related papers  (2023-11-13T10:46:35Z)
• WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model [92.90127398282209]
  This paper investigates the potential of integrating the most recent Large Language Models (LLMs) and existing visual grounding and robotic grasping system. We introduce the WALL-E (Embodied Robotic WAiter load lifting with Large Language model) as an example of this integration. We deploy this LLM-empowered system on the physical robot to provide a more user-friendly interface for the instruction-guided grasping task.
  arXiv  Detail & Related papers  (2023-08-30T11:35:21Z)
• Language-Driven Representation Learning for Robotics [115.93273609767145]
  Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. We introduce a framework for language-driven representation learning from human videos and captions. We find that Voltron's language-driven learning outperform the prior-of-the-art, especially on targeted problems requiring higher-level control.
  arXiv  Detail & Related papers  (2023-02-24T17:29:31Z)

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.