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HapCompass: A Rotational Haptic Device for Contact-Rich Robotic Teleoperation

Xiangshan Tan, Jingtian Ji, Tianchong Jiang, Pedro Lopes, Matthew R. Walter

Abstract

The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive directional cues within wearable teleoperation interfaces remains a bottleneck. Existing solutions, such as non-directional vibrations from handheld controllers, provide limited information, while vibrotactile arrays are prone to perceptual interference. To address these limitations, we propose HapCompass, a novel, low-cost wearable haptic device that renders 2D directional cues by mechanically rotating a single linear resonant actuator (LRA). We evaluated HapCompass's ability to convey directional cues to human operators and showed that it increased the success rate, decreased the completion time and the maximum contact force for teleoperated manipulation tasks when compared to vision-only and non-directional feedback baselines. Furthermore, we conducted a preliminary imitation-learning evaluation, suggesting that the directional feedback provided by HapCompass enhances the quality of demonstration data and, in turn, the trained policy. We release the design of the HapCompass device along with the code that implements our teleoperation interface: https://ripl.github.io/HapCompass/.

HapCompass: A Rotational Haptic Device for Contact-Rich Robotic Teleoperation

Abstract

The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive directional cues within wearable teleoperation interfaces remains a bottleneck. Existing solutions, such as non-directional vibrations from handheld controllers, provide limited information, while vibrotactile arrays are prone to perceptual interference. To address these limitations, we propose HapCompass, a novel, low-cost wearable haptic device that renders 2D directional cues by mechanically rotating a single linear resonant actuator (LRA). We evaluated HapCompass's ability to convey directional cues to human operators and showed that it increased the success rate, decreased the completion time and the maximum contact force for teleoperated manipulation tasks when compared to vision-only and non-directional feedback baselines. Furthermore, we conducted a preliminary imitation-learning evaluation, suggesting that the directional feedback provided by HapCompass enhances the quality of demonstration data and, in turn, the trained policy. We release the design of the HapCompass device along with the code that implements our teleoperation interface: https://ripl.github.io/HapCompass/.

Paper Structure

This paper contains 17 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Directional Haptic Devices
  4. Haptic Feedback in Robot Teleoperation
  5. The HapCompass System
  6. Hardware Design
  7. Teleoperation System Architecture
  8. Haptic Rendering and Control
  9. Experimental Evaluation
  10. Experiment 1: Directional Rendering Performance
  11. Experiment 2: Teleoperation Performance
  12. Experiment 3: Imitation Learning Evaluation
  13. Results
  14. Directional Rendering Results
  15. Teleoperation Results
  16. ...and 2 more sections

Figures (5)

  • Figure 3: Overview of the HapCompass teleoperation system. (a) An operator controls the robot via hand tracking while wearing our novel HapCompass device, which renders directional haptic feedback from the robot's sensors. (b) The robot arm executes the manipulation commands. (c) We evaluate the system on three contact-rich tasks: Key Insertion, USB Insertion, and Spaghetti Probing, with (left) our method achieving higher success than (right) baseline methods.
  • Figure 4: Exploded view of the HapCompass haptic device.
  • Figure 5: The overall HapCompass system architecture.
  • Figure 6: Direction-identification results presented as radar plots of the confusion matrices for the (a) 4-AFC and (b) 8-AFC tasks. Each colored wedge shows the distribution of participant responses for a given true direction. The overall accuracy and mean angular error across all trials are displayed in the center of each plot.
  • Figure 7: Teleoperation performance on the Key Insertion task across four teleoperation conditions (C1-C4). (a) Contact duration distributions. (b) Maximum contact force distributions. (c) Maximum bending torque distributions.