Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and indicate opportunities to integrate safety considerations into algorithms "by design". Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified

Chapter 3 How to Design Robots with Superpowers

Robots are often designed to increase efficiency. They are typically positioned in a particular field of application to replace humans, making someone’s work obsolete. But robots are able to complement the humans they work with through collaboration and the incorporation of the psychological strengths that robots can have in social situations. This chapter presents a co-design method based on a possibility-driven design approach and “robotic superpowers” (a concept that originated from Welge, Hassenzahl, Dörrenbächer and colleagues). Social robots can offer new, positive experiences and constructively impact work practices that incorporate their social strengths, such as endless patience or unbiased selection. To that end, the chapter describes a three-step design process by collecting existing work practices, defining scenario-specific robotic superpowers, and developing concepts for future robots. To illustrate possible outcomes, the authors showcase several exemplary concepts created using this new process

Sampling-based path planning for multi-robot systems with co-safe linear temporal logic specifications

© 2017, Springer International Publishing AG. This paper addresses the problem of path planning for multiple robots under high-level specifications given as syntactically co-safe linear temporal logic formulae. Most of the existing solutions use the notion of abstraction to obtain a discrete transition system that simulates the dynamics of the robot. Nevertheless, these solutions have poor scalability with the dimension of the configuration space of the robots. For problems with a single robot, sampling-based methods have been presented as a solution to alleviate this limitation. The proposed solution extends the idea of sampling methods to the multiple robot case. The method samples the configuration space of the robots to incrementally constructs a transition system that models the motion of all the robots as a group. This transition system is then combined with a Büchi automaton, representing the specification, in a Cartesian product. The product is updated with each expansion of the transition system until a solution is found. We also present a new algorithm that improves the performance of the proposed method by guiding the expansion of the transition system. The method is demonstrated with examples considering different number of robots and specifications

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Chapter 3 How to Design Robots with Superpowers

Robots are often designed to increase efficiency. They are typically positioned in a particular field of application to replace humans, making someone’s work obsolete. But robots are able to complement the humans they work with through collaboration and the incorporation of the psychological strengths that robots can have in social situations. This chapter presents a co-design method based on a possibility-driven design approach and “robotic superpowers” (a concept that originated from Welge, Hassenzahl, Dörrenbächer and colleagues). Social robots can offer new, positive experiences and constructively impact work practices that incorporate their social strengths, such as endless patience or unbiased selection. To that end, the chapter describes a three-step design process by collecting existing work practices, defining scenario-specific robotic superpowers, and developing concepts for future robots. To illustrate possible outcomes, the authors showcase several exemplary concepts created using this new process