  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.
