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Found 17 papers, 9 papers with code
Nesting Particle Filters for Experimental Design in Dynamical Systems
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization.
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing
Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making.
A Recursive Newton Method for Smoothing in Nonlinear State Space Models
In this paper, we use the optimization formulation of nonlinear Kalman filtering and smoothing problems to develop second-order variants of iterated Kalman smoother (IKS) methods.
Variational Gaussian filtering via Wasserstein gradient flows
We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering.
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics
We derive two efficient variational inference techniques to learn these representations and highlight the advantages of hierarchical infinite local regression models, such as dealing with non-smooth functions, mitigating catastrophic forgetting, and enabling parameter sharing and fast predictions.
Active Inference for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in the last decades.
Model-Based Reinforcement Learning via Stochastic Hybrid Models
Optimal control of general nonlinear systems is a central challenge in automation.
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
Optimal control under uncertainty is a prevailing challenge for many reasons.
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control.
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives.
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in the data.
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for autonomous agents.
A Nonparametric Off-Policy Policy Gradient
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes.
Receding Horizon Curiosity
Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion.
Stochastic Optimal Control as Approximate Input Inference
Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning.
Self-Paced Contextual Reinforcement Learning
Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots.
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
In order to show the monotonic improvement of our algorithm, we additionally conduct a theoretical analysis of our policy update scheme to derive a lower bound of the change in policy return between successive iterations.
