  Training a robot that engages with people is challenging; it is expensive to
directly involve people in the training process, which requires numerous data
samples. This paper presents an alternative approach for resolving this
problem. We propose a human path prediction network (HPPN) that generates a
user's future trajectory based on sequential robot actions and human responses
using a recurrent-neural-network structure. Subsequently, an
evolution-strategy-based robot training method using only the virtual human
movements generated using the HPPN is presented. It is demonstrated that our
proposed method permits sample-efficient training of a robotic guide for
visually impaired people. By collecting only 1.5 K episodes from real users, we
were able to train the HPPN and generate more than 100 K virtual episodes
required for training the robot. The trained robot precisely guided blindfolded
participants along a target path. Furthermore, using virtual episodes, we
investigated a new reward design that prioritizes human comfort during the
robot's guidance without incurring additional costs. This sample-efficient
training method is expected to be widely applicable to future robots that
interact physically with humans.
