From autonomy to alliance: Robotic foundation models must learn with us, not just for us.

Abstract

This Viewpoint urges reimagining of robotic foundation models, from treating the robot as a solitary, omnipotent agent to embracing a multiagent, alliance-aware paradigm. Alliance-aware models learn with humans and other robots, not merely for them, by embedding mechanisms that foster social interaction and generalization across heterogeneous partners. We outline six design pillars that cultivate such collaborative intelligence: interaction priors, partner modeling (machine theory of mind), modular and composable policies, norm adaptation, trust-aware memory, and communication. Together, these pillars empower robots to fluidly switch social roles, adapt to unfamiliar collaborators, and coordinate robustly within dynamic multiagent ecologies spanning homes, factories, clinics, and field operations.