Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

TL;DR

This paper compares two types of haptic guidance in robotics, proposes a combined approach, and finds that user prediction-based guidance offers better subjective experience, with the combined method improving alignment without sacrificing performance.

Contribution

It introduces a combined haptic guidance method that leverages deep learning to improve user experience and intention alignment in robotic assistance.

Findings

UPHG yields better naturalness and comfort scores.

CombHG reduces user intention disagreement.

Combined approach maintains performance while enhancing subjective experience.

Abstract

The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classified into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the difference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, than OAHG. In addition, the CombHG that we proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.