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From autonomy to alliance: Robotic foundation models must learn with us, not just for us
Sharmita Dey1, Robert Riener1, Strahinja Dosen2
1Institute of Robotics and Intelligent Systems (IRIS) - Sensory-Motor Systems Lab, ETH Zurich, Zurich, Switzerland.
Science Robotics
|April 22, 2026
Summary
Robotic foundation models should evolve from single agents to alliance-aware systems. These models will learn collaboratively with humans and robots, fostering social interaction and generalization across diverse partners for enhanced coordination.
Area of Science:
- Robotics
- Artificial Intelligence
- Multiagent Systems
Background:
- Current robotic foundation models often treat robots as solitary agents.
- This paradigm limits collaborative potential with humans and other robots.
- A shift towards multiagent, alliance-aware systems is needed.
Purpose of the Study:
- To propose a reimagined paradigm for robotic foundation models.
- To foster collaborative intelligence through social interaction and generalization.
- To enable robots to learn with and adapt to diverse partners.
Main Methods:
- Outlining six design pillars for collaborative robotic intelligence:
- Interaction priors, partner modeling (machine theory of mind), modular policies, norm adaptation, trust-aware memory, and communication.
- Focusing on mechanisms for social interaction and generalization across heterogeneous partners.
Main Results:
- The proposed pillars enable robots to fluidly switch social roles.
- Robots can adapt to unfamiliar collaborators and coordinate robustly.
- This facilitates effective operation within dynamic multiagent ecologies.
Conclusions:
- Reimagining robotic foundation models as alliance-aware systems is crucial.
- The six design pillars provide a framework for cultivating collaborative intelligence.
- This approach enhances robot adaptability and coordination in human-robot and robot-robot teams.
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