80 citations · 82 across the 6 of their papers we have counts for
6 papers · 1 filter
Least-Restrictive Multi-Agent Collision Avoidance via Deep Meta Reinforcement Learning and Optimal Control
Salar Asayesh, Mo Chen, Mehran Mehrandezh +1
Multi-agent collision-free trajectory planning and control subject to different goal requirements and system dynamics has been extensively studied, and is gaining recent attention…
FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking
Mo Chen, Sylvia L. Herbert, Haimin Hu +5
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computat…
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
Karen Leung, Edward Schmerling, Mengxuan Zhang +4
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is en…
Prediction-Based Reachability for Collision Avoidance in Autonomous Driving
Anjian Li, Liting Sun, Wei Zhan +2
Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal meth…
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
Anjian Li, Somil Bansal, Georgios Giovanis +3
In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed. Specifically, a Convolutional Neural Network…
TTR-Based Reward for Reinforcement Learning with Implicit Model Priors
Xubo Lyu, Mo Chen
Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-ineff…