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In this work, we develop a scalable, local trajectory optimization algorithmthat enables robots to interact with other robots. It has been shown thatagents' interactions can be successfully captured in game-theoreticformulations, where the interaction outcome can be best modeled via theequilibria of the underlying dynamic game. However, it is typically challengingto compute equilibria of dynamic games as it involves simultaneously solving aset of coupled optimal control problems. Existing solvers operate in acentralized fashion and do not scale up tractably to multiple interactingagents. We enable scalable distributed game-theoretic planning by leveragingthe structure inherent in multi-agent interactions, namely, interactionsbelonging to the class of dynamic potential games. Since equilibria of dynamicpotential games can be found by minimizing a single potential function, we canapply distributed and decentralized control techniques to seek equilibria ofmulti-agent interactions in a scalable and distributed manner. We compare theperformance of our algorithm with a centralized interactive planner in a numberof simulation studies and demonstrate that our algorithm results in betterefficiency and scalability. We further evaluate our method in hardwareexperiments involving multiple quadcopters.

Distributed Potential iLQR: Scalable Game-Theoretic Trajectory Planning for Multi-Agent Interactions