160 citations · 403 across the 56 of their papers we have counts for
6 papers · 1 filter
Safe and Resilient Multi-vehicle Trajectory Planning Under Adversarial Intruder
Somil Bansal, Mo Chen, Claire J. Tomlin
Provably safe and scalable multi-vehicle trajectory planning is an important and urgent problem. Hamilton-Jacobi (HJ) reachability is an ideal tool for analyzing such safety-critic…
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Somil Bansal, Roberto Calandra, Kurtland Chua +2
Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to…
Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances
Somil Bansal, Mo Chen, Sylvia Herbert +1
Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied t…
Provably Safe and Robust Drone Routing via Sequential Path Planning: A Case Study in San Francisco and the Bay Area
Mo Chen, Somil Bansal, Ken Tanabe +1
Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Hamilt…
Goal-Driven Dynamics Learning via Bayesian Optimization
Somil Bansal, Roberto Calandra, Ted Xiao +2
Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Ther…
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
Sylvia L. Herbert, Mo Chen, SooJean Han +3
Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for…