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Emergent Behaviors in Multi-Agent Human-Robot Teams

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Principal Investigators:

Stefano Ermon and Dorsa Sadigh

TRI Liaison:

Nikos Arechiga

Project Summary

Develop data-driven models of the latent structure behind human decision making in the context of multi-agent mixed-autonomy interactions (e.g. traffic intersections, shared roads, human-robot teaming, and mutual adaptation).

Models of people’s intentions and interactions will improve safety and reliability and may open new avenues for human-robot interactions and teaming. Real world settings include unprotected left turns, merging, etc.

Research Goals

Develop variable generative models to:

  1. Generate multi-agent behaviors indistinguishable from training data.
  2. Perform multi-agent intent inference in an unsupervised way.
  3. Learn teaming behaviors and adaptations.

Models will be evaluated in simulation and using real-world data.