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Sketch Interface for Teleoperation of Mobile Manipulator to Enable Intuitive and Intended Operation: A Proof of Concept

Yuka Iwanaga, Masayoshi Tsuchinaga, Kosei Tanada, Yuji Nakamura, Takemitsu Mori, Takashi Yamamoto

TL;DR

The paper tackles the problem of enabling intuitive yet effective teleoperation of a mobile manipulator by introducing a sketch-based interface that leverages robot autonomy to reduce operator workload. It presents a proof-of-concept interface and validates it through two within-subject studies: a broad exploration of natural sketch instructions across 27 tasks, and a direct comparison against a conventional axis-control interface on five grasp tasks. Results show that sketches elicit natural cues (eg, C-shaped symbols for grasping and lines/arrows for navigation) and generally reduce workload, though pose-estimation accuracy and fine-grained control remain bottlenecks, occasionally increasing task times or user frustration. The work demonstrates potential for accessible, intuitive human-robot collaboration in home and service contexts and outlines concrete directions to improve pose estimation, feedback, and evaluation across diverse users and longer tasks.

Abstract

Recent advancements in robotics have underscored the need for effective collaboration between humans and robots. Traditional interfaces often struggle to balance robot autonomy with human oversight, limiting their practical application in complex tasks like mobile manipulation. This study aims to develop an intuitive interface that enables a mobile manipulator to autonomously interpret user-provided sketches, enhancing user experience while minimizing burden. We implemented a web-based application utilizing machine learning algorithms to process sketches, making the interface accessible on mobile devices for use anytime, anywhere, by anyone. In the first validation, we examined natural sketches drawn by users for 27 selected manipulation and navigation tasks, gaining insights into trends related to sketch instructions. The second validation involved comparative experiments with five grasping tasks, showing that the sketch interface reduces workload and enhances intuitiveness compared to conventional axis control interfaces. These findings suggest that the proposed sketch interface improves the efficiency of mobile manipulators and opens new avenues for integrating intuitive human-robot collaboration in various applications.

Sketch Interface for Teleoperation of Mobile Manipulator to Enable Intuitive and Intended Operation: A Proof of Concept

TL;DR

The paper tackles the problem of enabling intuitive yet effective teleoperation of a mobile manipulator by introducing a sketch-based interface that leverages robot autonomy to reduce operator workload. It presents a proof-of-concept interface and validates it through two within-subject studies: a broad exploration of natural sketch instructions across 27 tasks, and a direct comparison against a conventional axis-control interface on five grasp tasks. Results show that sketches elicit natural cues (eg, C-shaped symbols for grasping and lines/arrows for navigation) and generally reduce workload, though pose-estimation accuracy and fine-grained control remain bottlenecks, occasionally increasing task times or user frustration. The work demonstrates potential for accessible, intuitive human-robot collaboration in home and service contexts and outlines concrete directions to improve pose estimation, feedback, and evaluation across diverse users and longer tasks.

Abstract

Recent advancements in robotics have underscored the need for effective collaboration between humans and robots. Traditional interfaces often struggle to balance robot autonomy with human oversight, limiting their practical application in complex tasks like mobile manipulation. This study aims to develop an intuitive interface that enables a mobile manipulator to autonomously interpret user-provided sketches, enhancing user experience while minimizing burden. We implemented a web-based application utilizing machine learning algorithms to process sketches, making the interface accessible on mobile devices for use anytime, anywhere, by anyone. In the first validation, we examined natural sketches drawn by users for 27 selected manipulation and navigation tasks, gaining insights into trends related to sketch instructions. The second validation involved comparative experiments with five grasping tasks, showing that the sketch interface reduces workload and enhances intuitiveness compared to conventional axis control interfaces. These findings suggest that the proposed sketch interface improves the efficiency of mobile manipulators and opens new avenues for integrating intuitive human-robot collaboration in various applications.

Paper Structure

This paper contains 30 sections, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Remote operation interface for robots
  4. Sketch Interface
  5. Proof of Concept for the Sketch Interface
  6. Selection of Grasping Tasks
  7. Experiment 1: Comprehensive User Survey on Sketch Instructions
  8. System Overview
  9. Tasks to be Conducted
  10. User Evaluation Design
  11. Experiment 2: Comparing Sketch and Conventional Interfaces
  12. System Overview
  13. User Interface
  14. Function Details
  15. User Evaluation Design
  16. ...and 15 more sections

Figures (10)

  • Figure 1: UI for evaluation application. The participants comprehend the task by referring to the description texts (1) and images (2), and write sketch instructions(4) in area (3).
  • Figure 2: Experimental environment with system overview.
  • Figure 3: Conventional interface UI. (1) View mode, (2) Navigation mode, (3) Arm mode, and (4) Gripper mode
  • Figure 7: Learning model for estimating grasp orientation.
  • Figure 8: Graph categorizing sketches by task category.
  • ...and 5 more figures