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Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Tom Blau, Lionel Ott, Fabio Ramos
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like -greedy. This…
OCTNet: Trajectory Generation in New Environments from Past Experiences
Weiming Zhi, Tin Lai, Lionel Ott +2
Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and unders…
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments
Lukas Schmid, Michael Pantic, Raghav Khanna +3
The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary informat…
Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produ…
Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction
Weiming Zhi, Lionel Ott, Fabio Ramos
Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments. This requires…
Learning to Plan Hierarchically from Curriculum
Philippe Morere, Lionel Ott, Fabio Ramos
We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: (i) We…
Bayesian optimisation under uncertain inputs
Rafael Oliveira, Lionel Ott, Fabio Ramos
Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however,…