Kernelized movement primitives

TL;DR: NDPs are proposed that make predictions in trajectory distribution space as opposed to prior policy learning methods where actions represent the raw control space and outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks for both imitation and reinforcement learning setups.

TL;DR: In this article, a kernelized treatment is introduced to alleviate explicit basis functions when learning orientation trajectories associated with high-dimensional inputs, which allows for learning orientation trajectory associated with arbitrary desired points that comprise orientation and angular velocities.

TL;DR: In this article , the authors present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environments to complete the task.

TL;DR: Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems, and are well suited to generate motor commands for artificial systems like robots.

TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

TL;DR: A review of machine learning methods employing positive definite kernels, ranging from binary classifiers to sophisticated methods for estimation with structured data, which include nonlinear functions as well as functions defined on nonvectorial data.

TL;DR: Dynamical movement primitives is presented, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques, and its properties are evaluated in motor control and robotics.