A2I

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Mutual Alignment Transfer Learning from Simulation to the Real World

Abstract - Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, these can [...]

Mutual Alignment Transfer Learning from Simulation to the Real World 2017-09-18T22:35:47+00:00

Adversarial Domain Adaptation

Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While the model is optimised for the training domain it will deliver degraded performance in application domains that underlie distributional shifts [...]

Adversarial Domain Adaptation 2017-09-18T22:39:46+00:00

Probabilistic Prediction of Perception Performance

Learn from Experience: Probabilistic Prediction of Perception Performance to Avoid Failure Abstract –Despite the significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging, time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their [...]

Probabilistic Prediction of Perception Performance 2017-05-09T12:09:30+00:00