Abstract
Ad hoc teamwork is a research topic in multi-agent systems whereby an
agent (the “ad hoc agent”) must successfully collaborate with a set of
unknown agents (the “teammates”) without any prior coordination or
communication protocol. However, research in ad hoc teamwork is
predominantly focused on agent-only teams, but not in agent-human teams,
which we believe is an exciting research avenue and has enormous
application potential in human-robot teams. This paper will tap into
this potential by proposing HOTSPOT, the first framework for ad hoc
teamwork in human-robot teams. Our framework comprises two main modules,
addressing the two key challenges in the interaction between a robot
acting as the ad hoc agent and human teammates. First, a
decision-theoretic module that is responsible for all
task-related decision making (task identification, teammate
identification, and planning). Second, a communication module
that uses natural language processing in order to parse all
communication between the robot and the human. To evaluate our
framework, we use a task where a mobile robot and a human cooperatively
collect objects in an open space, illustrating the main features of our
framework in a real-world task.