Search Results for author:
Found 4 papers, 2 papers with code
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
In this paper, we address this challenge by presenting Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving, without using any real-world data.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages.
Bayesian Learning-Based Adaptive Control for Safety Critical Systems
We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties.
