222,422 research outputs found
Autonomous learning and reproduction of complex sequences: a multimodal architecture for bootstraping imitation games
This paper introduces a control architecture
for the learning of complex sequence of gestures
applied to autonomous robots. The architecture
is designed to exploit the robot internal
sensory-motor dynamics generated by
visual, proprioceptive, and predictive informations
in order to provide intuitive behaviors
in the purpose of natural interactions
with humans
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. Current trends in reinforcement learning (RL) focus on
complex representations of dynamics and policies, which have yielded impressive
results in solving a variety of hard control tasks. However, this new
sophistication and extremely over-parameterized models have come with the cost
of an overall reduction in our ability to interpret the resulting policies. In
this paper, we take inspiration from the control community and apply the
principles of hybrid switching systems in order to break down complex dynamics
into simpler components. We exploit the rich representational power of
probabilistic graphical models and derive an expectation-maximization (EM)
algorithm for learning a sequence model to capture the temporal structure of
the data and automatically decompose nonlinear dynamics into stochastic
switching linear dynamical systems. Moreover, we show how this framework of
switching models enables extracting hierarchies of Markovian and
auto-regressive locally linear controllers from nonlinear experts in an
imitation learning scenario.Comment: 2nd Annual Conference on Learning for Dynamics and Contro
A new approach for designing self-organizing systems and application to adaptive control
There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed
Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning
Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions
A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics
Autonomous vehicles and driving technologies have received notable attention
in the past decades. In autonomous driving systems, \textcolor{black}{the}
information of vehicle dynamics is required in most cases for designing of
motion planning and control algorithms. However, it is nontrivial for
identifying a global model of vehicle dynamics due to the existence of strong
non-linearity and uncertainty. Many efforts have resorted to machine learning
techniques for building data-driven models, but it may suffer from
interpretability and result in a complex nonlinear representation. In this
paper, we propose a deep learning framework relying on an interpretable Koopman
operator to build a data-driven predictor of the vehicle dynamics. The main
idea is to use the Koopman operator for representing the nonlinear dynamics in
a linear lifted feature space. The approach results in a global model that
integrates the dynamics in both longitudinal and lateral directions. As the
core contribution, we propose a deep learning-based extended dynamic mode
decomposition (Deep EDMD) algorithm to learn a finite approximation of the
Koopman operator. Different from other machine learning-based approaches, deep
neural networks play the role of learning feature representations for EDMD in
the framework of the Koopman operator. Simulation results in a high-fidelity
CarSim environment are reported, which show the capability of the Deep EDMD
approach in multi-step prediction of vehicle dynamics at a wide operating
range. Also, the proposed approach outperforms the EDMD method, the multi-layer
perception (MLP) method, and the Extreme Learning Machines-based EDMD
(ELM-EDMD) method in terms of modeling performance. Finally, we design a linear
MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the
controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm
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