Towards Learning of Safety Knowledge from Human Demonstrations

Future autonomous service robots are intended
to operate in open and complex environments. This in turn
implies complications ensuring safe operation. The tenor of few
available investigations is the need for dynamically assessing
operational risks. Furthermore, a new kind of hazards being
implicated by the robot’s capability to manipulate the environment
occurs: hazardous environmental object interactions.
One of the open questions in safety research is integrating
safety knowledge into robotic systems, enabling these systems
behaving safety-conscious in hazardous situations. In this paper
a safety procedure is described, in which learning of safety
knowledge from human demonstration is considered. Within
the procedure, a task is demonstrated to the robot, which
observes object-to-object relations and labels situational data
as commanded by the human. Based on this data, several
supervised learning techniques are evaluated used for finally
extracting safety knowledge. Results indicate that Decision
Trees allow interesting opportunities.