Molecular HCI

Shape-changing User Interfaces attract growing interest in Human-Computer Interaction. Modular robotics offer a great opportunity for their implementation. However, the current theoretical and technical advances of modular robotics are fragmented and little centered on the user. To unify existing work and center future research on the user, we perform a systematic literature review enabling us to build a unifying space for the design of modular shape-changing user interfaces.Our aim is to bridge the gap between HCI and robotics. We relate properties of different domains and identify inconsistencies to structure the design space. Towards this aim, we conduct a thorough cross-disciplinary survey to propose: 1) a set of design properties at the scale of the interface (macro-scale) and at the scale of the modules (micro-scale) and 2) the impact of these properties on each other. This paper can be used to describe and compare existing modular shape-changing UIs and generate new design ideas by building upon knowledge from robotics and HCI.

MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

Swarm-dular quadcopter: bringing swarm robotics and modular robotics together

In this paper, we present a new concept in robotics, which we call as swarm-dular robotics, by bringing together swarm robotics and modular robotics. We propose a swarm-dular quadcopter, which is a modular quadcopter, where, multiple wheeled ground robots (AGVs), which are part of a heterogenous swarm or multi-robotic(agent) system (MRS or MAS), attach/dock together to form an aerial quadcopter robot. We provide a conceptual design and demonstrate the proposed system using simulation experiments in Robot Operating System (ROS). Behavior of the system as both a swarm and a modular quadcopter UAV are demonstrated.