Context-Aware Command Understanding for Tabletop Scenarios

• URL: http://arxiv.org/abs/2410.06355v2
• Date: Thu, 10 Oct 2024 10:59:22 GMT
• Title: Context-Aware Command Understanding for Tabletop Scenarios
• Authors: Paul Gajewski, Antonio Galiza Cerdeira Gonzalez, Bipin Indurkhya,
• Abstract summary: This paper presents a novel hybrid algorithm designed to interpret natural human commands in tabletop scenarios. By integrating multiple sources of information, including speech, gestures, and scene context, the system extracts actionable instructions for a robot. We discuss the strengths and limitations of the system, with particular focus on how it handles multimodal command interpretation.
• Score: 1.7082212774297747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel hybrid algorithm designed to interpret natural human commands in tabletop scenarios. By integrating multiple sources of information, including speech, gestures, and scene context, the system extracts actionable instructions for a robot, identifying relevant objects and actions. The system operates in a zero-shot fashion, without reliance on predefined object models, enabling flexible and adaptive use in various environments. We assess the integration of multiple deep learning models, evaluating their suitability for deployment in real-world robotic setups. Our algorithm performs robustly across different tasks, combining language processing with visual grounding. In addition, we release a small dataset of video recordings used to evaluate the system. This dataset captures real-world interactions in which a human provides instructions in natural language to a robot, a contribution to future research on human-robot interaction. We discuss the strengths and limitations of the system, with particular focus on how it handles multimodal command interpretation, and its ability to be integrated into symbolic robotic frameworks for safe and explainable decision-making.

Related papers

• One to rule them all: natural language to bind communication, perception and action [0.9302364070735682]
  This paper presents an advanced architecture for robotic action planning that integrates communication, perception, and planning with Large Language Models (LLMs) The Planner Module is the core of the system where LLMs embedded in a modified ReAct framework are employed to interpret and carry out user commands. The modified ReAct framework further enhances the execution space by providing real-time environmental perception and the outcomes of physical actions.
  arXiv  Detail & Related papers  (2024-11-22T16:05:54Z)
• Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
  Differentiable simulation has become a powerful tool for system identification. Our approach calibrates object properties by using information from the robot, without relying on data from the object itself. We demonstrate the effectiveness of our method on a low-cost robotic platform.
  arXiv  Detail & Related papers  (2024-10-04T20:48:38Z)
• 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)
• Learning to Act from Actionless Videos through Dense Correspondences [87.1243107115642]
  We present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments. Our method leverages images as a task-agnostic representation, encoding both the state and action information, and text as a general representation for specifying robot goals. We demonstrate the efficacy of our approach in learning policies on table-top manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2023-10-12T17:59:23Z)
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control [140.48218261864153]
  We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control. Our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training.
  arXiv  Detail & Related papers  (2023-07-28T21:18:02Z)
• 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)
• 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)
• Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach [0.0]
  We present a neurosymbolic architecture for coupling language-guided visual reasoning with robot manipulation. A non-expert human user can prompt the robot using unconstrained natural language, providing a referring expression (REF), a question (VQA) or a grasp action instruction. We generate a 3D vision-and-language synthetic dataset of tabletop scenes in a simulation environment to train our approach and perform extensive evaluations in both synthetic and real-world scenes.
  arXiv  Detail & Related papers  (2022-10-03T12:21:45Z)
• Summarizing a virtual robot's past actions in natural language [0.3553493344868413]
  We show how a popular dataset that matches robot actions with natural language descriptions designed for an instruction following task can be repurposed to serve as a training ground for robot action summarization work. We propose and test several methods of learning to generate such summaries, starting from either egocentric video frames of the robot taking actions or intermediate text representations of the actions used by an automatic planner.
  arXiv  Detail & Related papers  (2022-03-13T15:00:46Z)
• INVIGORATE: Interactive Visual Grounding and Grasping in Clutter [56.00554240240515]
  INVIGORATE is a robot system that interacts with human through natural language and grasps a specified object in clutter. We train separate neural networks for object detection, for visual grounding, for question generation, and for OBR detection and grasping. We build a partially observable Markov decision process (POMDP) that integrates the learned neural network modules.
  arXiv  Detail & Related papers  (2021-08-25T07:35:21Z)
• Language Understanding for Field and Service Robots in a Priori Unknown Environments [29.16936249846063]
  This paper provides a novel learning framework that allows field and service robots to interpret and execute natural language instructions. We use language as a "sensor" -- inferring spatial, topological, and semantic information implicit in natural language utterances. We incorporate this distribution in a probabilistic language grounding model and infer a distribution over a symbolic representation of the robot's action space.
  arXiv  Detail & Related papers  (2021-05-21T15:13:05Z)

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.