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Found 8 papers, 1 papers with code
Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization
This paper presents a novel methodology that uses surrogate models in the form of neural networks to reduce the computation time of simulation-based optimization of a reference trajectory.
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories
Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely.
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations
Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space.
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning
We present an unsupervised method to map RL trajectories into a feature space where distance represents the relative difference in system behavior due to hidden parameters.
Uncertainty Quantification for Competency Assessment of Autonomous Agents
For safe and reliable deployment in the real world, autonomous agents must elicit appropriate levels of trust from human users.
Competency Assessment for Autonomous Agents using Deep Generative Models
For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform.
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data
The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations.
Real-Time Object Pose Estimation with Pose Interpreter Networks
In this work, we introduce pose interpreter networks for 6-DoF object pose estimation.
