PROGrasp: Pragmatic Human-Robot Communication for Object Grasping

• URL: http://arxiv.org/abs/2309.07759v4
• Date: Fri, 5 Apr 2024 07:53:45 GMT
• Title: PROGrasp: Pragmatic Human-Robot Communication for Object Grasping
• Authors: Gi-Cheon Kang, Junghyun Kim, Jaein Kim, Byoung-Tak Zhang,
• Abstract summary: Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Inspired by pragmatics, we introduce a new IOG task, Pragmatic-IOG, and the corresponding dataset, Intention-oriented Multi-modal Dialogue (IM-Dial) Prograsp performs Pragmatic-IOG by incorporating modules for visual grounding, question asking, object grasping, and most importantly, answer interpretation for pragmatic inference.
• Score: 22.182690439449278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interactive Object Grasping (IOG) is the task of identifying and grasping the desired object via human-robot natural language interaction. Current IOG systems assume that a human user initially specifies the target object's category (e.g., bottle). Inspired by pragmatics, where humans often convey their intentions by relying on context to achieve goals, we introduce a new IOG task, Pragmatic-IOG, and the corresponding dataset, Intention-oriented Multi-modal Dialogue (IM-Dial). In our proposed task scenario, an intention-oriented utterance (e.g., "I am thirsty") is initially given to the robot. The robot should then identify the target object by interacting with a human user. Based on the task setup, we propose a new robotic system that can interpret the user's intention and pick up the target object, Pragmatic Object Grasping (PROGrasp). PROGrasp performs Pragmatic-IOG by incorporating modules for visual grounding, question asking, object grasping, and most importantly, answer interpretation for pragmatic inference. Experimental results show that PROGrasp is effective in offline (i.e., target object discovery) and online (i.e., IOG with a physical robot arm) settings. Code and data are available at https://github.com/gicheonkang/prograsp.

Related papers

• Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine [0.8749675983608172]
  We focus on the task of retrieving target objects from open-vocabulary user instructions in a human-in-the-loop setting. We propose MultiRankIt, which is a novel approach for the learning-to-rank physical objects task.
  arXiv  Detail & Related papers  (2023-12-26T01:40:31Z)
• 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)
• Proactive Human-Robot Interaction using Visuo-Lingual Transformers [0.0]
  Humans possess the innate ability to extract latent visuo-lingual cues to infer context through human interaction. We propose a learning-based method that uses visual cues from the scene, lingual commands from a user and knowledge of prior object-object interaction to identify and proactively predict the underlying goal the user intends to achieve.
  arXiv  Detail & Related papers  (2023-10-04T00:50:21Z)
• 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)
• 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)
• Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? [54.442692221567796]
  Task specification is critical for engagement of non-expert end-users and adoption of personalized robots. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use.
  arXiv  Detail & Related papers  (2022-04-23T19:39:49Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)
• 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)

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.