Behind any robot task or interaction is a representation that should (a) enable sufficient contextualization; (b) support any existing predefined, learned, and/or reusable skills onboard the robot; (c) be verifiable at design time and behave consistently at run-time; and (d) can be tested, executed, and modified for reuse on a variety of different robot morphologies. Capturing the end user's desired task or interaction as a computational artifact (i.e., a representation) has long played a pivotal role in robotics.

Many robotic subfields have traditionally employed a variety of different representational techniques, often borrowing from artificial intelligence (AI), to achieve their respective objectives. Examples include variants of LTL, logic, automated planning languages, languages for Belief-Desire-Intention, custom variations of these, and many more than we can list. The problem is that there is a lack of awareness of these representations in the research community, or how these representations can/should be applied.

Last year, UR-RAD focused on representation trends in robotics and best practices for adopting existing and new representations. This year, based on community feedback, we are planning on elevating the interaction between junior and senior members of the community.