  Guiding robots can not only detect close-range obstacles like other guiding
tools, but also extend its range to perceive the environment when making
decisions. However, most existing works over-simplified the interaction between
human agents and robots, ignoring the differences between individuals,
resulting in poor experiences for different users. To solve the problem, we
propose a data-driven guiding system to cope with the effect brighten by
individual differences. In our guiding system, we design a Human Motion
Predictor (HMP) and a Robot Dynamics Model (RDM) based on deep neural network,
the time convolutional network (TCN) is verified to have the best performance,
to predict differences in interaction between different human agents and
robots. To train our models, we collected datasets that records the
interactions from different human agents. Moreover, given the predictive
information of the specific user, we propose a waypoints selector that allows
the robot to naturally adapt to the user's state changes, which are mainly
reflected in the walking speed. We compare the performance of our models with
previous works and achieve significant performance improvements. On this basis,
our guiding system demonstrated good adaptability to different human agents.
Our guiding system is deployed on a real quadruped robot to verify the
practicability.
