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Hands-On Robotics: Enabling Communication Through Direct Gesture Control

Max Pascher, Alia Saad, Jonathan Liebers, Roman Heger, Jens Gerken, Stefan Schneegass, Uwe Gruene

TL;DR

The paper addresses the challenge of natural human–robot communication without auxiliary hardware by introducing four motion-based gestures that directly manipulate a robot's end effector. A within-subject study with 16 participants demonstrates high gesture discriminability from joint data using a random forest classifier, achieving a mean f1-score of 0.99 with an 80/20 training/test split and 0.91 in user-independent trials. The findings show that end-effector manipulation can serve as a robust, intuitive interface for HRI, with practical implications for simplifying interfaces and enabling closer human–robot collaboration. Overall, the work provides a compact, hardware-light pathway for direct gesture-based control in HRI, with strong potential for real-world deployment and extension to more gestures and user groups.

Abstract

Effective Human-Robot Interaction (HRI) is fundamental to seamlessly integrating robotic systems into our daily lives. However, current communication modes require additional technological interfaces, which can be cumbersome and indirect. This paper presents a novel approach, using direct motion-based communication by moving a robot's end effector. Our strategy enables users to communicate with a robot by using four distinct gestures -- two handshakes ('formal' and 'informal') and two letters ('W' and 'S'). As a proof-of-concept, we conducted a user study with 16 participants, capturing subjective experience ratings and objective data for training machine learning classifiers. Our findings show that the four different gestures performed by moving the robot's end effector can be distinguished with close to 100% accuracy. Our research offers implications for the design of future HRI interfaces, suggesting that motion-based interaction can empower human operators to communicate directly with robots, removing the necessity for additional hardware.

Hands-On Robotics: Enabling Communication Through Direct Gesture Control

TL;DR

The paper addresses the challenge of natural human–robot communication without auxiliary hardware by introducing four motion-based gestures that directly manipulate a robot's end effector. A within-subject study with 16 participants demonstrates high gesture discriminability from joint data using a random forest classifier, achieving a mean f1-score of 0.99 with an 80/20 training/test split and 0.91 in user-independent trials. The findings show that end-effector manipulation can serve as a robust, intuitive interface for HRI, with practical implications for simplifying interfaces and enabling closer human–robot collaboration. Overall, the work provides a compact, hardware-light pathway for direct gesture-based control in HRI, with strong potential for real-world deployment and extension to more gestures and user groups.

Abstract

Effective Human-Robot Interaction (HRI) is fundamental to seamlessly integrating robotic systems into our daily lives. However, current communication modes require additional technological interfaces, which can be cumbersome and indirect. This paper presents a novel approach, using direct motion-based communication by moving a robot's end effector. Our strategy enables users to communicate with a robot by using four distinct gestures -- two handshakes ('formal' and 'informal') and two letters ('W' and 'S'). As a proof-of-concept, we conducted a user study with 16 participants, capturing subjective experience ratings and objective data for training machine learning classifiers. Our findings show that the four different gestures performed by moving the robot's end effector can be distinguished with close to 100% accuracy. Our research offers implications for the design of future HRI interfaces, suggesting that motion-based interaction can empower human operators to communicate directly with robots, removing the necessity for additional hardware.
Paper Structure (19 sections, 1 figure)

This paper contains 19 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Related Work
  3. Gesture Input Interactions
  4. Study: Gesture Recognition Using a Robotic Arm
  5. Study Design
  6. Participants and Procedure
  7. Apparatus
  8. Results
  9. Objective Measures
  10. Subjective Measures
  11. Gesture Recognition
  12. Data Analysis
  13. Cross-Validation Classification
  14. User-Independent Classification
  15. Discussion
  16. ...and 4 more sections

Figures (1)

  • Figure 1: Measures of gestures, for (first) temporal, (second) spatial dimensions, and confusion matrices, for (third) inverse classification (cross-validation), the training sample $=$ 0.2, and (fourth) user-independent (cross-subject), with training sample $=$ 0.5.