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Guiding robots can not only detect close-range obstacles like other guidingtools, but also extend its range to perceive the environment when makingdecisions. However, most existing works over-simplified the interaction betweenhuman agents and robots, ignoring the differences between individuals,resulting in poor experiences for different users. To solve the problem, wepropose a data-driven guiding system to cope with the effect brighten byindividual differences. In our guiding system, we design a Human MotionPredictor (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 androbots. To train our models, we collected datasets that records theinteractions from different human agents. Moreover, given the predictiveinformation of the specific user, we propose a waypoints selector that allowsthe robot to naturally adapt to the user's state changes, which are mainlyreflected in the walking speed. We compare the performance of our models withprevious 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 thepracticability.

BVIP Guiding System with Adaptability to Individual Differences