Learning from demonstration (LfD) has been used to help robots to implement manipulation
tasks autonomously, in particular, to learn manipulation behaviors from observing the motion …

Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

We presented and evaluated an approach based on HMM, GMR, and dynamical systems to
allow robots to acquire new skills by imitation. Using HMM allowed us to get rid of the explicit …

In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main
strategies: contact model-based and contact model-free strategies. More specifically, we first …

The dynamical movement primitives (DMPs) model is a useful tool for efficient robotic
learning manipulation skills from human demonstrations and then generalizing these skills …

This paper presents the application of a statistical framework that allows to endow a
humanoid robot with the ability to perform a collaborative manipulation task with a human …

The automation of production lines in industrial scenarios implies solving different problems,
such as the flexibility to deploy robotic solutions to different production lines, usability to …

Similar to humans, robots benefit from interacting with their environment through a number
of different sensor modalities, such as vision, touch, sound. However, learning from different …

In order to improve road safety, many advanced driver assist systems (ADAS) have been
developed to support human-decision making and reduce driver workload. Currently, the …

We propose a framework based on imitation learning and self-learning to enable robots to
learn, improve, and generalize motor skills. The peg-in-hole task is important in …