Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes

The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.Comment: To be published in 2nd Annual Conference on Learning for Dynamics and Control (L4DC), Proceedings of Machine Learning Research 202

Controle por Modos Deslizantes de um Atuador Eletro-hidráulico com Compensação por Processo Gaussiano / Sliding Mode Control of an Electric-Hydraulic Actuator with Gaussian Process Compensation

O desenvolvimento de sistemas de controle precisos para atuadores eletro-hidráulicos depende de uma adequada compensação dos efeitos dinâmicos desconhecidos. Neste trabalho, um controlador por Modos Deslizantes é combinado com um compensador por Processo Gaussiano para proporcionar um adequado rastreamento de trajetória. Processo Gaussiano é uma conhecida estratégia de aprendizagem de máquinas que pode ser utilizada no reconhecimento de funções. As propriedades de convergência do sistema em malha fechada são analisadas pela Teoria de Estabilidade de Lyapunov. Resultados numéricos confirmam uma forte melhora no desempenho do controlador ao ser inserido o compensador proposto

Probabilistic Recurrent State-Space Models

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem

A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotic