A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance

This paper addresses the problem of cooperative transportation of an object rigidly grasped by $N$ robotic agents. In particular, we propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.Comment: Simulation results with 3 agents adde

Language-based sensing descriptors for robot object grounding

In this work, we consider an autonomous robot that is required to understand commands given by a human through natural language. Specifically, we assume that this robot is provided with an internal representation of the environment. However, such a representation is unknown to the user. In this context, we address the problem of allowing a human to understand the robot internal representation through dialog. To this end, we introduce the concept of sensing descriptors. Such representations are used by the robot to recognize unknown object properties in the given commands and warn the user about them. Additionally, we show how these properties can be learned over time by leveraging past interactions in order to enhance the grounding capabilities of the robot

Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments

This paper presents two novel control methodologies for the cooperative manipulation of an object by N robotic agents. Firstly, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Secondly, we propose a control protocol that guarantees predefined transient and steady-state performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid, and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudo-inverse to account for potential differences in the agents' power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

An Introduction to 3D User Interface Design

3D user interface design is a critical component of any virtual environment (VE) application. In this paper, we present a broad overview of three-dimensional (3D) interaction and user interfaces. We discuss the effect of common VE hardware devices on user interaction, as well as interaction techniques for generic 3D tasks and the use of traditional two-dimensional interaction styles in 3D environments. We divide most user interaction tasks into three categories: navigation, selection/manipulation, and system control. Throughout the paper, our focus is on presenting not only the available techniques, but also practical guidelines for 3D interaction design and widely held myths. Finally, we briefly discuss two approaches to 3D interaction design, and some example applications with complex 3D interaction requirements. We also present an annotated online bibliography as a reference companion to this article