Reset-free Trial-and-Error Learning for Robot Damage Recovery

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at https://youtu.be/IqtyHFrb3BU, code at https://github.com/resibots/chatzilygeroudis_2018_rt

MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of Intelligent & Robotic System

Geo-Design:

Geo-Design. Advances in bridging geo-information technology and design brings together a wide variety of contributions from authors with backgrounds in urban planning, landscape architecture, education and geo-information technology presenting the latest insights and applications of geodesign. Geo-Design is here understood as a hybridization of the concepts “Geo” – representing the modelling, analytical and visualisation capacities of GIS, and “Design” – representing spatial planning and design, turning existing situations into preferred ones. Through focusing on interdisciplinary design-related concepts and applications of GIS international experts share their recent findings and provide clues for the further development of geodesign. This is important since there is still much to do. Not only in the development of geo-information technology, but especially in bridging the gap with the design disciplines. The uptake on using GIS is still remarkably slow among landscape architects, urban designers and planners, and when utilised it is often restricted to the basic tasks of mapmaking and data access. Knowledge development and dissemination of applications of geodesign through research, publications and education, therefore, remain key factors. This publication draws upon the insights shared at the Geodesign Summit Europe held at the Delft University of Technology in 2014. All contributions in the book are double blind reviewed by experts in the field

Towards Personalized Explanations for AI Systems: Designing a Role Model for Explainable AI in Auditing

Due to a continuously growing repertoire of available methods and applications, Artificial Intelligence (AI) is becoming an innovation driver for most industries. In the auditing domain, initial approaches of AI have already been discussed in scientific discourse, but practical application is still lagging behind. Caused by a highly regulated environment, the explainability of AI is of particular relevance. Using semi-structured expert interviews, we identified stakeholder specific requirements regarding explainable AI (XAI) in auditing. To address the needs of all involved stakeholders a theoretical role model for AI systems has been designed based on a systematic literature review. The role model has been instantiated and evaluated in the domain of financial statement auditing using focus groups of domain experts. The resulting model offers a foundation for the development of AI systems with personalized explanations and an optimized usage of existing XAI methods