Incorporating Rich Social Interactions Into MDPs
This work addresses the challenge of incorporating complex social behaviors into robotics, which is crucial for developing more human-like autonomous agents, though it appears incremental as it builds on existing MDP frameworks.
The authors tackled the problem of enabling robots to engage in rich social interactions by formalizing a theory from microsociology and economics into an extended Social MDP, resulting in a robotic agent capable of zero-shot execution of social interactions in new environments with judgments closely aligning with human assessments.
Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology and economics can be formalized by extending a nested MDP where agents reason about arbitrary functions of each other's hidden rewards. This extended Social MDP allows us to encode the five basic interactions that underlie microsociology: cooperation, conflict, coercion, competition, and exchange. The result is a robotic agent capable of executing social interactions zero-shot in new environments; like humans it can engage socially in novel ways even without a single example of that social interaction. Moreover, the judgments of these Social MDPs align closely with those of humans when considering which social interaction is taking place in an environment. This method both sheds light on the nature of social interactions, by providing concrete mathematical definitions, and brings rich social interactions into a mathematical framework that has proven to be natural for robotics, MDPs.