80 citations · 113 across the 42 of their papers we have counts for
5 papers · 2 filters
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…
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…
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Andrea Bajcsy, Somil Bansal, Eli Bronstein +2
Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely in the…
Combining Optimal Control and Learning for Visual Navigation in Novel Environments
Somil Bansal, Varun Tolani, Saurabh Gupta +2
Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challeng…
A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics
Shromona Ghosh, Somil Bansal, Alberto Sangiovanni-Vincentelli +2
We consider the problem of extracting safe environments and controllers for reach-avoid objectives for systems with known state and control spaces, but unknown dynamics. In a given…