activity
20192022
most citedFaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

80 citations · 81 across the 2 of their papers we have counts for

collaborators

7 papers

cs.RO20221 cited

Computation of Regions of Attraction for Hybrid Limit Cycles Using Reachability: An Application to Walking Robots

Jason J. Choi, Ayush Agrawal, Koushil Sreenath +2

Contact-rich robotic systems, such as legged robots and manipulators, are often represented as hybrid systems. However, the stability analysis and region-of-attraction computation…

cs.RO202180 cited

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…

cs.RO2020

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

Somil Bansal, Claire Tomlin

Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical control systems. Its advantage…

cs.RO2020

Visual Navigation Among Humans with Optimal Control as a Supervisor

Varun Tolani, Somil Bansal, Aleksandra Faust +1

Real world visual navigation requires robots to operate in unfamiliar, human-occupied dynamic environments. Navigation around humans is especially difficult because it requires ant…

cs.RO2019

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…

cs.RO2019

A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning

Somil Bansal, Andrea Bajcsy, Ellis Ratner +2

Real-world autonomous systems often employ probabilistic predictive models of human behavior during planning to reason about their future motion. Since accurately modeling human be…