Deep representations for collaborative robotics

Abstract

Collaboration is an essential feature of human social interaction. Briefly, when two or more people agree on a common goal and a joint intention to reach that goal, they have to coordinate their actions to engage in joint actions, planning their courses of actions according to the actions of the other partners. The same holds for teams where the partners are people and robots, resulting on a collection of technical questions difficult to answer. Human-robot collaboration requires the robot to coordinate its behavior to the behaviors of the humans at different levels, e.g., the semantic level, the level of the content and behavior selection in the interaction, and low-level aspects such as the temporal dynamics of the interaction. This forces the robot to internalize information about the motions, actions and intentions of the rest of partners, and about the state of the environment. Furthermore, collaborative robots should select their actions taking into account additional human-aware factors such as safety, reliability and comfort. Current cognitive systems are usually limited in this respect as they lack the rich dynamic representations and the flexible human-aware planning capabilities needed to succeed in tomorrow human-robot collaboration tasks. Within this paper, we provide a tool for addressing this problem by using the notion of deep hybrid representations and the facilities that this common state representation offers for the tight coupling of planners on different layers of abstraction. Deep hybrid representations encode the robot and environment state, but also a robot-centric perspective of the partners taking part in the joint activity.