On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Improving human-robot interactivity for tele-operated industrial and service robot applications

In industrial robotics applications, teach pendant has been widely used by human operators to pre-define action trajectories for robot manipulators to execute as primitives. This hard-coding approach is only good for low-mix-highvolume jobs with sparse trajectory way-points. In this paper, we present a novel industrial robotic system designed for applications where human-robot interaction is key for efficient execution of actions such as high-mix-low-volume jobs. The proposed system comprises a robot manipulator that controls a tool (such as a soldering iron) to interact with the required workpiece, a networking server for remote tele-operation, and an integrated user interface that allows the human operator to better perceive the remote operation and to execute actions with greater ease. A user study is conducted to understand the merits of the proposed system. Results indicate that human can operate the system with ease and complete tasks more quickly and that the system can improve application efficiency

Human to robot hand motion mapping methods: review and classification

In this article, the variety of approaches proposed in literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macro-categories the great quantity of presented methods, that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology and declared goals of the mappings. Firstly, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under six categories: direct joint, direct Cartesian, taskoriented, dimensionality reduction based, pose recognition based and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors’ point of view regarding future desirable trends are reported.This work was supported in part by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL under Grant 870133 and in part by the Spanish Government under Grant PID2020-114819GB-I00.Peer ReviewedPostprint (published version

Immersive Teleoperation of the Eye Gaze of Social Robots Assessing Gaze-Contingent Control of Vergence, Yaw and Pitch of Robotic Eyes

International audienceThis paper presents a new teleoperation system – called stereo gaze-contingent steering (SGCS) – able to seamlessly control the vergence, yaw and pitch of the eyes of a humanoid robot – here an iCub robot – from the actual gaze direction of a remote pilot. The video stream captured by the cameras embedded in the mobile eyes of the iCub are fed into an HTC Vive R Head-Mounted Display equipped with an SMI R binocular eye-tracker. The SGCS achieves the effective coupling between the eye-tracked gaze of the pilot and the robot's eye movements. SGCS both ensures a faithful reproduction of the pilot's eye movements – that is perquisite for the readability of the robot's gaze patterns by its interlocutor – and maintains the pilot's oculomotor visual clues – that avoids fatigue and sickness due to sensorimotor conflicts. We here assess the precision of this servo-control by asking several pilots to gaze towards known objects positioned in the remote environment. We demonstrate that we succeed in controlling vergence with similar precision as eyes' azimuth and elevation. This system opens the way for robot-mediated human interactions in the personal space, notably when objects in the shared working space are involved

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201

Improving human-robot interactivity for tele-operated industrial and service robot applications

In industrial robotics applications, teach pendant has been widely used by human operators to pre-define action trajectories for robot manipulators to execute as primitives. This hard-coding approach is only good for low-mix-highvolume jobs with sparse trajectory way-points. In this paper, we present a novel industrial robotic system designed for applications where human-robot interaction is key for efficient execution of actions such as high-mix-low-volume jobs. The proposed system comprises a robot manipulator that controls a tool (such as a soldering iron) to interact with the required workpiece, a networking server for remote tele-operation, and an integrated user interface that allows the human operator to better perceive the remote operation and to execute actions with greater ease. A user study is conducted to understand the merits of the proposed system. Results indicate that human can operate the system with ease and complete tasks more quickly and that the system can improve application efficiency