4.2k citations · 4.4k across the 40 of their papers we have counts for
9 papers · 1 filter
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
Vitor Guizilini, Ransalu Senanayake, Fabio Ramos
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world…
Occ-Traj120: Occupancy Maps with Associated Trajectories
Tin Lai, Weiming Zhi, Fabio Ramos
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour. Predicting the dynamic path by observing the agent and its surrounding…
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…
BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, Dieter Fox
We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator. As simulators become more sophisticated and able…
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu, Fabio Ramos
Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex…
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
Kelvin Hsu, Fabio Ramos
In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible param…
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,…