We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple
objects and their affordances from RGB images. Our AffordanceNet has two branches: an …

Despite the development of a large number of mobile manipulation robots, very few
platforms can demonstrate the required strength and mechanical sturdiness to …

Purpose This paper aims to develop an adaptable control architecture for electrohydraulic
humanoid robots (HYDROïD) that emulate the functionality of the human nervous system …

Solving mobile manipulation tasks in inaccessible and dangerous environments is an
important application of robots to support humans. Example domains are construction and …

We present a new method to translate videos to commands for robotic manipulation using
Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the …

This work introduces a framework for the Cartesian control of multi-legged, highly redundant
robots. The proposed framework allows the untrained user to perform complex motion tasks …

Performing a demanding manipulation task with a multi-legged loco-manipulation platform
may require the exploitation of multiple external contacts with different environment surfaces …

Develo** a high physical performance robotic manipulation platform with considerable
power density, strength and resilience is not a trivial task and frequently leads to heavy and …

In this paper, we present an implementation of a bi-manual teleoperation system, controlled
by a human through three-dimensional (3D) skeleton extraction. The input data is given from …

This paper proposes a real-time (RT) control architecture based on Xenomai, an RT
embedded Linux, to control a service robot along with non-real-time (NRT) robot operating …