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06/15/2023
A Recursive Newton Method for Smoothing in Nonlinear State Space Models
In this paper, we use the optimization formulation of nonlinear Kalman f...
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03/11/2023
Variational Gaussian filtering via Wasserstein gradient flows
We present a novel approach to approximate Gaussian and mixture-of-Gauss...
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11/02/2022
Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics
Well-calibrated probabilistic regression models are a crucial learning c...
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06/01/2022
Active Inference for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite signif...
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11/11/2021
Model-Based Reinforcement Learning for Stochastic Hybrid Systems
Optimal control of general nonlinear systems is a central challenge in a...
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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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03/29/2021
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions
Trajectory optimization and model predictive control are essential techn...
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02/25/2021
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Across machine learning, the use of curricula has shown strong empirical...
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11/10/2020
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Probabilistic regression techniques in control and robotics applications...
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05/04/2020
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for...
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01/08/2020
A Nonparametric Offpolicy Policy Gradient
Reinforcement learning (RL) algorithms still suffer from high sample com...
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10/08/2019
Receding Horizon Curiosity
Sample-efficient exploration is crucial not only for discovering rewardi...
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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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10/07/2019