11 citations · 24 across the 18 of their papers we have counts for
4 papers · 1 filter
Distributed Potential iLQR: Scalable Game-Theoretic Trajectory Planning for Multi-Agent Interactions
Zach Williams, Jushan Chen, Negar Mehr
In this work, we develop a scalable, local trajectory optimization algorithm that enables robots to interact with other robots. It has been shown that agents' interactions can be s…
Efficient Constrained Multi-Agent Trajectory Optimization using Dynamic Potential Games
Maulik Bhatt, Yixuan Jia, Negar Mehr
Although dynamic games provide a rich paradigm for modeling agents' interactions, solving these games for real-world applications is often challenging. Many real-world interactive…
Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories
Talha Kavuncu, Ayberk Yaraneri, Negar Mehr
Many robotic applications involve interactions between multiple agents where an agent's decisions affect the behavior of other agents. Such behaviors can be captured by the equilib…
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
Haruki Nishimura, Negar Mehr, Adrien Gaidon +1
Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited…