Dynamic movement primitives in robotics: A tutorial survey
Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …
A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
One-shot visual imitation learning via meta-learning
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to
acquire a wide variety of skills quickly and efficiently in complex unstructured environments …
acquire a wide variety of skills quickly and efficiently in complex unstructured environments …
Preparing for the unknown: Learning a universal policy with online system identification
We present a new method of learning control policies that successfully operate under
unknown dynamic models. We create such policies by leveraging a large number of training …
unknown dynamic models. We create such policies by leveraging a large number of training …
Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions
We consider an imitation learning approach to model robot point-to-point (also known as
discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each …
discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each …
[BOOK][B] Learning to learn with gradients
CB Finn - 2018 - search.proquest.com
Humans have a remarkable ability to learn new concepts from only a few examples and
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …
quickly adapt to unforeseen circumstances. To do so, they build upon their prior experience …
Learning parameterized skills
We introduce a method for constructing skills capable of solving tasks drawn from a
distribution of parameterized reinforcement learning problems. The method draws example …
distribution of parameterized reinforcement learning problems. The method draws example …
Inverse KKT: Learning cost functions of manipulation tasks from demonstrations
Inverse optimal control (IOC) assumes that demonstrations are the solution to an optimal
control problem with unknown underlying costs, and extracts parameters of these underlying …
control problem with unknown underlying costs, and extracts parameters of these underlying …
Learning task-parameterized dynamic movement primitives using mixture of GMMs
Task-parameterized skill learning aims at adaptive motion encoding to new situations. While
existing approaches for task-parameterized skill learning have demonstrated good …
existing approaches for task-parameterized skill learning have demonstrated good …
Toward orientation learning and adaptation in cartesian space
As a promising branch of robotics, imitation learning emerges as an important way to
transfer human skills to robots, where human demonstrations represented in Cartesian or …
transfer human skills to robots, where human demonstrations represented in Cartesian or …