Task Parametrized Gaussian Mixture Models (TP-GMM) are a sample-efficient method for
learning object-centric robot manipulation tasks. However, there are several open …

Learning complex robot motions necessarily demands to have models that are able to
encode and retrieve full-pose trajectories when tasks are defined in operational spaces …

The research community is puzzled with words like skill, action, atomic unit and others when
describing robots' capabilities. However, for giving the possibility to integrate capabilities in …

For robots to work alongside humans and perform in unstructured environments, they must
learn new motion skills and adapt them to unseen situations on the fly. This demands …

Learning from Demonstration (LfD) provides an intuitive and fast approach to program
robotic manipulators. Task parameterized representations allow easy adaptation to new …

For performing robotic manipulation tasks, the core problem is determining suitable
trajectories that fulfill the task requirements. Various approaches to compute such …

Efficiently coordinating different types of robots is an important enabler for many commercial
and industrial automation tasks. Here, we present a distributed framework that enables a …

In recent decades, advancements in motion learning have enabled robots to acquire new
skills and adapt to unseen conditions in both structured and unstructured environments. In …

In contrast to humans and animals who naturally execute seamless motions, learning and
smoothly executing sequences of actions remains a challenge in robotics. This letter …

Complex manipulation tasks can contain various execution branches of primitive skills in
sequence or in parallel under different scenarios. Manual specifications of such branching …