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10/07/2022
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
Monte Carlo methods have become increasingly relevant for control of non...
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10/24/2021
A Differentiable Newton-Euler Algorithm for Real-World Robotics
Obtaining dynamics models is essential for robotics to achieve accurate ...
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05/17/2021
Stochastic Control through Approximate Bayesian Input Inference
Optimal control under uncertainty is a prevailing challenge in control, ...
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03/10/2021
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Discrete-time stochastic optimal control remains a challenging problem f...
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11/03/2020
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
A limitation of model-based reinforcement learning (MBRL) is the exploit...
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10/19/2020
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
In this work, we examine a spectrum of hybrid model for the domain of mu...
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10/01/2020
Active Inference or Control as Inference? A Unifying View
Active inference (AI) is a persuasive theoretical framework from computa...
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03/03/2020
A fast Monte Carlo test for preferential sampling
The preferential sampling of locations chosen to observe a spatio-tempor...
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10/31/2019
A general framework for estimating the spatio-temporal distribution of a species using multiple data types
Species distribution models (SDMs) are useful tools to help ecologists q...
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10/07/2019
Stochastic Optimal Control as Approximate Input Inference
Optimal control of stochastic nonlinear dynamical systems is a major cha...
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09/13/2018