  This paper presents a hybrid robot cognitive architecture, CRAM, that enables
robot agents to accomplish everyday manipulation tasks. It addresses five key
challenges that arise when carrying out everyday activities. These include (i)
the underdetermined nature of task specification, (ii) the generation of
context-specific behavior, (iii) the ability to make decisions based on
knowledge, experience, and prediction, (iv) the ability to reason at the levels
of motions and sensor data, and (v) the ability to explain actions and the
consequences of these actions. We explore the computational foundations of the
CRAM cognitive model: the self-programmability entailed by physical symbol
systems, the CRAM plan language, generalized action plans and
implicit-to-explicit manipulation, generative models, digital twin knowledge
representation & reasoning, and narrative-enabled episodic memories. We
describe the structure of the cognitive architecture and explain the process by
which CRAM transforms generalized action plans into parameterized motion plans.
It does this using knowledge and reasoning to identify the parameter values
that maximize the likelihood of successfully accomplishing the action. We
demonstrate the ability of a CRAM-controlled robot to carry out everyday
activities in a kitchen environment. Finally, we consider future extensions
that focus on achieving greater flexibility through transformational learning
and metacognition.
