Many regression algorithms, one unified model: A review
Regression is the process of learning relationships between inputs and continuous outputs
from example data, which enables predictions for novel inputs. The history of regression is …
from example data, which enables predictions for novel inputs. The history of regression is …
A survey of deep learning techniques for autonomous driving
The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
Model-based reinforcement learning: A survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
Nonholonomic mobile robots' trajectory tracking model predictive control: a survey
Model predictive control (MPC) theory has gained attention with the recent increase in the
processing power of computers that are now able to perform the needed calculations for this …
processing power of computers that are now able to perform the needed calculations for this …
Cautious model predictive control using gaussian process regression
Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
Incremental learning algorithms and applications
Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
Robust constrained learning-based NMPC enabling reliable mobile robot path tracking
This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive
Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots …
Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots …
Active learning of inverse models with intrinsically motivated goal exploration in robots
We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity
(SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which …
(SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which …
Neural network and jacobian method for solving the inverse statics of a cable-driven soft arm with nonconstant curvature
The solution of the inverse kinematics problem of soft manipulators is essential to generate
paths in the task space. The inverse kinematics problem of constant curvature or piecewise …
paths in the task space. The inverse kinematics problem of constant curvature or piecewise …
A bio-inspired incremental learning architecture for applied perceptual problems
We present a biologically inspired architecture for incremental learning that remains
resource-efficient even in the face of very high data dimensionalities (> 1000) that are …
resource-efficient even in the face of very high data dimensionalities (> 1000) that are …