This work aims at reviewing, analyzing and comparing a range of state-of-the-art
approaches to inertial parameter identification in the context of robotics. We introduce …

Generally, structural uncertainty of the robot dynamics system refers to model error caused
by parameter identification, unstructured uncertainty is the unmodeled dynamic …

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 …

Efficient and high-precision identification of dynamic parameters is the basis of model-based
robot control. Firstly, this paper designed the structure and control system of the developed …

Modeling physical systems with neural networks (NN) requires expert architects to
determine the best number of nodes, layers and activation functions. For complex systems …

Abstract Purpose Using the da Vinci Research Kit (dVRK), we propose and experimentally
demonstrate transfer learning (Xfer) of dynamics between different configurations and robots …

A personal-computer-based and a Raspberry Pi single-board computer-based virtual force
sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper …

Robot inverse dynamics modeling is performed mainly via standard system identification
and/or machine learning techniques. In this paper we part from the theoretical framework of …

This thesis investigates the modalities of use of tactile feedback in the context of human-
robot interaction, distributed state estimation and inertial parameter identification. This …

Autonome Unterwassermanipulation ist heutzutage immer noch eine offene
Forschungsaufgabe. Dieses Kapitel beschreibt Ansätze, einige der offenen …