  Effective Human-Robot Interaction (HRI) is fundamental to seamlessly
integrating robotic systems into our daily lives. However, current
communication modes require additional technological interfaces, which can be
cumbersome and indirect. This paper presents a novel approach, using direct
motion-based communication by moving a robot's end effector. Our strategy
enables users to communicate with a robot by using four distinct gestures --
two handshakes ('formal' and 'informal') and two letters ('W' and 'S'). As a
proof-of-concept, we conducted a user study with 16 participants, capturing
subjective experience ratings and objective data for training machine learning
classifiers. Our findings show that the four different gestures performed by
moving the robot's end effector can be distinguished with close to 100%
accuracy. Our research offers implications for the design of future HRI
interfaces, suggesting that motion-based interaction can empower human
operators to communicate directly with robots, removing the necessity for
additional hardware.
