Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …

In human-robot collaboration the robot's behavior impacts the worker's safety, comfort and
acceptance of the robotic system. In this paper we address the problem of how to improve …

Recent success of machine learning in many domains has been overwhelming, which often
leads to false expectations regarding the capabilities of behavior learning in robotics. In this …

The optical motion capture (MoCap) sensor provides an effective way to capture human
motions and transform them into valuable data that can be applied to certain tasks, eg robot …

We aim to develop an efficient programming method for equipping service robots with the
skill of performing sign language motions. This paper addresses the problem of transferring …

In this work, we present a reconfigurable data glove design to capture different modes of
human hand–object interactions, which are critical in training embodied artificial intelligence …

Imitation of the upper body motions of human demonstrators or animation characters to
human-shaped robots is studied in this paper. We present a pipeline for motion retargeting …

Robot learning problems are limited by physical constraints, which make learning successful
policies for complex motor skills on real systems unfeasible. Some reinforcement learning …

We propose a method for generating a configuration space path that closely follows a
desired task space path despite the presence of obstacles. We formalize closeness via two …

This paper presents a mirroring approach, inspired by the neuroscience discovery of the
mirror neurons, to transfer demonstrated manipulation actions to robots. Designed to …