Action-based confidence sharing and collective decision making.

Joint action research explores how multiple humans can coordinate their movements to achieve common goals. When there is uncertainty about the joint goal, individuals need to integrate their perceptual information of the environment to collaboratively determine their new goal. To ensure that a group reaches a consensus about the goal, collective decision making among the individuals is required.

Integrative experiments require a shared theoretical and methodological basis.

Creating an integrated design space can be successful only if researchers agree on how to define and measure a certain phenomenon of interest. Adversarial collaborations and mathematical modeling can aid in reaching the necessary level of agreement when researchers depart from different theoretical perspectives.

Learning from humans to build social cognition among robots.

Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans.