Abstract:
This thesis investigates the problem of inferring the underlying dynamic model of individual agents of a multiagent system (MAS) and using these models to shape the MAS’s behavior using robots extrinsic to the MAS. We investigate (a) how an observer can infer the latent task and inter-agent interaction constraints from the agents’ motion and (b) how the observer can elicit a desired behavior out of the MAS by orchestrating its interactions with robots. The ability to learn individual dynamics models of an aggregated system has several applications such as learning local rules in biological swarms that give rise to emergent behavior and learning tactics of an adversarial multirobot team. Likewise, the ability to shape behavior using extrinsic robots can be used to defend against an adversarial team of robots and guide humans using robots as in social navigation.

The first part of this thesis focuses on the model learning problem. We model agents as integrators that solve a reactive optimization problem to calculate velocities for mediating between goal-directed motions and collision avoidance with other agents. We develop several estimators that allow an observer to infer this model’s parameters and show that the learned parameters indeed rationalize the observed motions. Necessary identifiability conditions are derived that guarantee correct inference. Our proposed estimators include adaptive observers, Kalman filters and several inverse optimization algorithms that are robust to both measurement noise and model mismatch. To demonstrate this robustness, we evaluate these estimators on a pedestrian dataset and learn each pedestrian’s desired velocity, aggressiveness coefficients and safety margins with walls, obstacles and other pedestrians.

The second part of this thesis focuses on eliciting a desired behavior out of the MAS by inducing interactions with robots. While the theory we develop is general, we consider the dog-sheep herding problem as a use case that requires controlling dog robots to repel sheep agents from a critical zone. We incorporate non-collocated feedback linearization in an optimization-based framework to compute the desired controls for the dogs. Both centralized and distributed implementations are developed to cater to the scalability, feasibility and budget-efficiency objectives. We validate the correctness of these controllers in multiple experiments on the CMU multirobot arena. We also develop a robust extension of these controllers, which we term control-barrier function based semidefinite programs (CBF-SDPs), that guarantee zone defense despite uncertainty in the sheep’s dynamics. Finally, we conclude this thesis with an integration of the robust model learning algorithms with robust control algorithms followed by experimental validation on the multirobot arena.

Thesis Committee Members:
Changliu Liu, Co-chair
Katia Sycara, Co-chair
Zac Manchester
Mario Santillo, Ford Motor Company

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