From the Turing test to science fiction: the challenges of social robotics

The Turing test (1950) sought to distinguish whether a speaker engaged in a computer talk was a human or a machine [6]. Science fiction has immortalized several humanoid robots full of humanity, and it is nowadays speculating about the role the human being and the machine may play in this “pas à deux” in which we are irremissibly engaged [12]. Where is current robotics research heading to? Industrial robots are giving way to social robots designed to aid in healthcare, education, entertainment and services. In the near future, robots will assist disabled and elderly people, do chores, act as playmates for youngsters and adults, and even work as nannies and reinforcement teachers. This poses new requirements to robotics research, since social robots must be easy to program by non-experts [10], intrinsically safe [3], able to perceive and manipulate deformable objects [2, 8], tolerant to inaccurate perceptions and actions [4, 7] and, above all, they must be endowed with a strong learning capacity [1, 9] and a high adaptability [14] to non-predefined and dynamic environments. Taking as an example projects developed at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC), some of the scientific, technological and ethical challenges [5, 11, 13] that this robotic evolution entails will be showcased.Peer ReviewedPostprint (author’s final draft

User evaluation of an interactive learning framework for single-arm and dual-arm robots

The final publication is available at link.springer.comSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett’s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.Peer ReviewedPostprint (author's final draft

ARoMA-V2: Assistive Robotic Manipulation Assistance with Computer Vision and Voice Recognition

We have designed and developed a handy alternative control method, called ARoMA-V2 (Assistive Robotic Manipulation Assistance with computer Vision and Voice recognition), for controlling assistive robotic manipulators based on computer vision and user voice recognition. Potential advantages of ARoMA-V2 over the traditional alternatives include: providing completely hands-free operation; helping a user to maintain a better working posture; Allowing the user to work in postures that otherwise would not be effective for operating an assistive robotic manipulator (i.e., reclined in a chair or bed); supporting task specific commands; providing the user with different levels of intelligent autonomous manipulation assistances; giving the user the feeling that he or she is still in control at any moment; and being compatible with different types of new and existing assistive robotic manipulators

Requirement Gathering Problems: Environmental Issues in Robot Development

This paper deals with the importance of environment consideration in developed countries while collecting the requirement from customer to make robot that would address the question “Why robots should be made more precisely according to the environment needs? “In developed countries, robots are used in manufacturing work as well as in performing the hazardous tasks such as bomb-disposal. So, there is a need to pay attention towards making the robots that can fit perfectly to some extent in environment to be utilized more efficiently. A lot of money, effort and time is spent on making the robots .But what if such a worth costing robot fails to fit in the operational environment? The best way to solve this problem is proposed in this paper which is to make the environment as a part of Requirement gathering process carrying high importance in robot making process to make the robots more Operational and suitable for the working environment .Like the other main attributes in requirement gathering process such as user requirements, system requirements and external requirements, there should be an attribute “Environmental requirements” which will automatically put emphasis on the considering also the environment as a main subject to  pay heed

Ai Motion Control – A Generic Approach to Develop Control Policies for Robotic Manipulation Tasks

AbstractCurrent robotic solutions are able to manage specialized tasks, but they cannot perform intelligent actions which are based on experience. Autonomous robots that are able to succeed in complex environments like production plants need the ability to customize their capabilities. With the usage of artificial intelligence (AI) it is possible to train robot control policies without explicitly programming how to achieve desired goals. We introduce AI Motion Control (AIMC) a generic approach to develop control policies for diverse robots, environments and manipulation tasks. For safety reasons, but also to save investments and development time, motion control policies can first be trained in simulation and then transferred to real applications. This work uses the descriptive study I according to Blessing and Chakrabarti and is about the identification of this research gap. We combine latest motion control and reinforcement learning results and show the potential of AIMC for robotic technologies with industrial use cases.</jats:p