4.2k citations · 4.4k across the 40 of their papers we have counts for
46 papers
Dual Online Stein Variational Inference for Control and Dynamics
Lucas Barcelos, Alexander Lambert, Rafael Oliveira +3
Model predictive control (MPC) schemes have a proven track record for delivering aggressive and robust performance in many challenging control tasks, coping with nonlinear system d…
BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao, Aaron Klein, Matthias Seeger +3
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion enco…
A User's Guide to Calibrating Robotics Simulators
Bhairav Mehta, Ankur Handa, Dieter Fox +1
Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world…
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Weiming Zhi, Tin Lai, Lionel Ott +1
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This pape…
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Carolyn Matl, Yashraj Narang, Dieter Fox +2
Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stoch…
Fast Uncertainty Quantification for Deep Object Pose Estimation
Guanya Shi, Yifeng Zhu, Jonathan Tremblay +4
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transf…
Stein Variational Model Predictive Control
Alexander Lambert, Adam Fishman, Dieter Fox +2
Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but rem…
Learning to Plan Optimally with Flow-based Motion Planner
Tin Lai, Fabio Ramos
Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The ma…
Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
Sebastian Haan, Fabio Ramos, Dietmar Müller
A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost. We p…
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Anthony Tompkins, Rafael Oliveira, Fabio Ramos
We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, the…
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Rel Guzman, Rafael Oliveira, Fabio Ramos
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided b…
Online Domain Adaptation for Occupancy Mapping
Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDA…
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Muhammad Asif Rana, Anqi Li, Dieter Fox +3
Robotic tasks often require motions with complex geometric structures. We present an approach to learn such motions from a limited number of human demonstrations by exploiting the…
Estimating Motion Uncertainty with Bayesian ICP
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Accurate uncertainty estimation associated with the pose transformation between two 3D point clouds is critical for autonomous navigation, grasping, and data fusion. Iterative clos…
Intrinsic Exploration as Multi-Objective RL
Philippe Morere, Fabio Ramos
Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy woul…
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,…