  As social beings, much human behavior is predicated on social context - the
ambient social state that includes cultural norms, social signals, individual
preferences, etc. In this paper, we propose a socially-aware task and motion
planning algorithm that considers social context to generate appropriate and
effective plans in human social environments (HSEs). The key strength of our
proposed approach is that it explicitly models how potential actions not only
affect objective cost, but also transform the social context in which it plans
and acts. We investigate strategies to limit the complexity of our algorithm,
so that our planner will remain tractable for mobile platforms in complex HSEs
like hospitals and factories. The planner will also consider the relative
importance and urgency of its tasks, which it uses to determine when it is and
is not appropriate to violate social expectations to achieve its objective.
This social awareness will allow robots to understand a fundamental rule of
society: just because something makes your job easier, does not make it the
right thing to do!
  To our knowledge, the proposed work is the first task and motion planning
approach that supports socially intelligent robot policy for HSEs. Through this
ongoing work, robots will be able to understand, respect, and leverage social
context accomplish tasks both acceptably and effectively in HSEs.
