Search Results for author:
Found 9 papers, 2 papers with code
Learning Risk-aware Costmaps for Traversability in Challenging Environments
One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move.
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.
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.
Robotics
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.
Deep Learning Tubes for Tube MPC
This uncertainty may come from errors in learning (due to a lack of data, for example), or may be inherent to the system.
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.
Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms
Recent work from the reinforcement learning community has shown that Evolution Strategies are a fast and scalable alternative to other reinforcement learning methods.
Multiagent Systems
MPC-Inspired Neural Network Policies for Sequential Decision Making
In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making.
