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Found 4 papers, 3 papers with code
Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information
We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space.
Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks
Additionally, if given an opportunity to prepare the environment in advance (a special case of anticipatory planning), our planner improves overall cost by 11%.
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem.
Generating High-Quality Explanations for Navigation in Partially-Revealed Environments
We present an approach for generating natural language explanations of high-level behavior of autonomous agents navigating in partially-revealed environments.
