  Ambiguity and noise in natural language instructions create a significant
barrier towards adopting autonomous systems into safety critical workflows
involving humans and machines. In this paper, we propose to build on recent
advances in electrophysiological monitoring methods and augmented reality
technologies, to develop alternative modes of communication between humans and
robots involved in large-scale proximal collaborative tasks. We will first
introduce augmented reality techniques for projecting a robot's intentions to
its human teammate, who can interact with these cues to engage in real-time
collaborative plan execution with the robot. We will then look at how
electroencephalographic (EEG) feedback can be used to monitor human response to
both discrete events, as well as longer term affective states while execution
of a plan. These signals can be used by a learning agent, a.k.a an affective
robot, to modify its policy. We will present an end-to-end system capable of
demonstrating these modalities of interaction. We hope that the proposed system
will inspire research in augmenting human-robot interactions with alternative
forms of communications in the interests of safety, productivity, and fluency
of teaming, particularly in engineered settings such as the factory floor or
the assembly line in the manufacturing industry where the use of such wearables
can be enforced.
