  The security issue of mobile robots has attracted considerable attention in
recent years. In this paper, we propose an intelligent physical attack to trap
mobile robots into a preset position by learning the obstacle-avoidance
mechanism from external observation. The salient novelty of our work lies in
revealing the possibility that physical-based attacks with intelligent and
advanced design can present real threats, while without prior knowledge of the
system dynamics or access to the internal system. This kind of attack cannot be
handled by countermeasures in traditional cyberspace security. To practice, the
cornerstone of the proposed attack is to actively explore the complex
interaction characteristic of the victim robot with the environment, and learn
the obstacle-avoidance knowledge exhibited in the limited observations of its
behaviors. Then, we propose shortest-path and hands-off attack algorithms to
find efficient attack paths from the tremendous motion space, achieving the
driving-to-trap goal with low costs in terms of path length and activity
period, respectively. The convergence of the algorithms is proved and the
attack performance bounds are further derived. Extensive simulations and
real-life experiments illustrate the effectiveness of the proposed attack,
beckoning future investigation for the new physical threats and defense on
robotic systems.
