The word “robot” frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect—they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term “robots,” and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots’ physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future.

Classical methods of modelling and mapping robot work cells are time consuming, expensive and involve expert knowledge. We present a novel approach to mapping and cell setup using modern Head Mounted Displays (HMDs) that possess self-localisation and mapping capabilities. We leveraged these capabilities to create a point cloud of the environment and build an OctoMap - a voxel occupancy grid representation of the robot's workspace for path planning. Through the use of Augmented Reality (AR) interactions, the user can edit the created Octomap and add safety zones. We perform comprehensive tests of the HoloLens' depth sensing capabilities and the quality of the resultant point cloud. A high-end laser scanner is used to provide the ground truth for the evaluation of the point cloud quality. The amount of false-positive and false-negative voxels in the OctoMap are also tested.

Hand guidance of robots has proven to be a useful tool both for programming trajectories and in kinesthetic teaching. However hand guidance is usually relegated to robots possessing joint-torque sensors (JTS). Here we propose to extend hand guidance to robots lacking those sensors through the use of an Augmented Reality (AR) device, namely Microsoft's Hololens. Augmented reality devices have been envisioned as a helpful addition to ease both robot programming and increase situational awareness of humans working in close proximity to robots. We reference the robot by using a registration algorithm to match a robot model to the spatial mesh. The in-built hand tracking capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations we achieve a completely sensorless hand guidance without any need to build a dynamic model of the robot itself. We did the first tests our approach on a commonly used industrial manipulator, the KUKA KR-5.

Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots intuitively. Going beyond, a robot can learn tasks by user demonstrations through kinesthetic teaching, enabling robots to generalise tasks and further reducing the need for reprogramming. However, hand guidance is still mostly relegated to collaborative robots. Here we propose a method that does not require any sensors on the robot or in the robot cell, by using a Microsoft HoloLens augmented reality head mounted display. We reference the robot using a registration algorithm to match the robot model to the spatial mesh. The in-built hand tracking and localisation capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations and propagating it down through the kinematic chain, we achieve a generalised hand guidance without the need to build a dynamic model of the robot itself. We tested our approach on a commonly used industrial manipulator, the KUKA KR-5.

Over the next few years, more and more autonomous mobile robot systems will find their way into modern shop floors. However, it will be necessary to provide human-machine interfaces for interventions in unexpected situations like system-deadlocks, algorithm failures or inabilities. Using virtual or mixed reality-technologies, multi-modal teleoperation offers potential for being a suitable human-machine interface. Essential challenges in this field are, among others, a real-time remote control, a time-efficient and holistic environment detection using multiple sensors, a noise-reduced visualization of sensor-data, and capabilities of object recognition. This paper summarizes research results regarding an architecture capable of a near realtime, interoperable, and operator-supporting teleoperation. The focus of this paper is on a method to efficiently process and visualize point-clouds to meet high frame rate demands of virtual reality applications. To provide near real-time feedback of the robot and its environment over large distances, the presented method is capable to segment known objects from unknown objects to reduce bandwidth requirements. The results of this paper were evaluated using a industrial articulated robotic arm for teleoperation via a long distance UDP/IP communication.