Active learning in robotics: A review of control principles
Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …
learning demands both analysis and action. This is a review of active learning in robotics …
An integrated kinematic calibration and dynamic identification method with only static measurements for serial robot
Y Yuan, W Sun - IEEE/ASME Transactions on Mechatronics, 2023 - ieeexplore.ieee.org
In this article, we propose an integrated calibration and identification method that can
identify kinematic and dynamic parameters at the same time. Only a series of static …
identify kinematic and dynamic parameters at the same time. Only a series of static …
Kernel methods and gaussian processes for system identification and control: A road map on regularized kernel-based learning for control
The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …
behavior consists of two steps. First, a model of the system is learned from input–output data …
Kinodynamic model identification: A unified geometric approach
A robot's dynamic model depends on both the kinematic and mass-inertial parameters of a
robot. Robot model identification therefore typically begins with kinematic identification; the …
robot. Robot model identification therefore typically begins with kinematic identification; the …
A black-box physics-informed estimator based on gaussian process regression for robot inverse dynamics identification
Learning the inverse dynamics of robots directly from data, adopting a black-box approach,
is interesting for several real-world scenarios where limited knowledge about the system is …
is interesting for several real-world scenarios where limited knowledge about the system is …
Gaussian processes model-based control of underactuated balance robots
Control of underactuated balance robot requires external subsystem trajectory tracking and
internal unstable subsystem balancing with limited control authority. We present a learning …
internal unstable subsystem balancing with limited control authority. We present a learning …
[HTML][HTML] User-empowered secure privacy-preserving authentication scheme for Digital Twin
Digital Twin (DT) is a revolutionary technology changing how a smart manufacturing industry
carries out its day-to-day activities. DT can provide numerous advantages such as real-time …
carries out its day-to-day activities. DT can provide numerous advantages such as real-time …
Safety-guaranteed learning-predictive control for aggressive autonomous vehicle maneuvers
This paper seeks to securely maximize the maneuverability of autonomous vehicles using a
nonlinear learning-predictive controller to enable safety-guaranteed aggressive vehicle …
nonlinear learning-predictive controller to enable safety-guaranteed aggressive vehicle …
Model-based policy search using monte carlo gradient estimation with real systems application
In this article, we present a model-based reinforcement learning (MBRL) algorithm named
Monte Carlo probabilistic inference for learning control (MC-PILCO). This algorithm relies on …
Monte Carlo probabilistic inference for learning control (MC-PILCO). This algorithm relies on …
General Hamiltonian Neural Networks for Dynamic Modeling: Handling Sophisticated Constraints Automatically and Achieving Coordinates Free
Y Liu, X Wang, Y Song, B Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Embedding the Hamiltonian formalisms into neural networks (NNs) enhances the reliability
and precision of data-driven models, in which substantial research has been conducted …
and precision of data-driven models, in which substantial research has been conducted …