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Found 5 papers, 2 papers with code
Cognitive Mapping and Planning for Visual Navigation
The accumulated belief of the world enables the agent to track visited regions of the environment.
Visual Navigation Among Humans with Optimal Control as a Supervisor
Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website.
Combining Optimal Control and Learning for Visual Navigation in Novel Environments
Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories.
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors.
Robotics
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed.
