Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a mapping …

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 …

This paper proposes an enhanced robot skill learning system considering both motion
generation and trajectory tracking. During robot learning demonstrations, dynamic …

Task-parameterized models of movements aim at automatically adapting movements to new
situations encountered by a robot. The task parameters can, for example, take the form of …

Robots are becoming safe and smart enough to work alongside people not only on
manufacturing production lines, but also in spaces such as houses, museums, or hospitals …

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …

This paper proposes an interaction learning method for collaborative and assistive robots
based on movement primitives. The method allows for both action recognition and human …

We propose an approach to estimate 3D human pose in real world units from a single RGBD
image and show that it exceeds performance of monocular 3D pose estimation approaches …

The framework of dynamic movement primitives (DMPs) contains many favorable properties
for the execution of robotic trajectories, such as indirect dependence on time, response to …

Industrial robots are expected to share the same workspace with human workers and work
in cooperation with humans to improve the productivity and maintain the quality of products …