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 …

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 …

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the
world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting …

We present a systematic survey of computational research in humanrobot interaction (HRI)
over the past decade. Computational HRI is the subset of the field that is specifically …

To engage in cooperative activities with human partners, robots have to possess basic
interactive abilities and skills. However, programming such interactive skills is a challenging …

Human–robot collaboration in industrial applications is a challenging robotic task. Human
working together with the robot at a workplace to complete a task may create unpredicted …

Much recent work in robot learning from demonstration has focused on automatically
segmenting continuous task demonstrations into simpler, reusable primitives. However …

One of the big challenges in robotics today is to learn from human users that are
inexperienced in interacting with robots but yet are often used to teach skills flexibly to other …

Accuracy and stability have in recent studies been emphasized as the two major ingredients
to learn robot motions from demonstrations with dynamical systems. Several approaches …

Conditional Neural Movement Primitives (CNMPs) is a learning from demonstration
framework that is designed as a robotic movement learning and generation system built on …