Effects of alarms on control of robot teams

Annunciator driven supervisory control (ADSC) is a widely used technique for directing human attention to control systems otherwise beyond their capabilities. ADSC requires associating abnormal parameter values with alarms in such a way that operator attention can be directed toward the involved subsystems or conditions. This is hard to achieve in multirobot control because it is difficult to distinguish abnormal conditions for states of a robot team. For largely independent tasks such as foraging, however, self-reflection can serve as a basis for alerting the operator to abnormalities of individual robots. While the search for targets remains unalarmed the resulting system approximates ADSC. The described experiment compares a control condition in which operators perform a multirobot urban search and rescue (USAR) task without alarms with ADSC (freely annunciated) and with a decision aid that limits operator workload by showing only the top alarm. No differences were found in area searched or victims found, however, operators in the freely annunciated condition were faster in detecting both the annunciated failures and victims entering their cameras' fields of view. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

A Data Set for Fault Detection Research on Component-Based Robotic Systems

Wienke J, Meyer zu Borgsen S, Wrede S. A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul L, Damian D, Aitken JM, eds. Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. Vol 9716. Springer International Publishing; 2016: 339-350.Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available

Making High-Performance Robots Safe and Easy to Use for an Introduction to Computing

Robots are a popular platform for introducing computing and artificial intelligence to novice programmers. However, programming state-of-the-art robots is very challenging, and requires knowledge of concurrency, operation safety, and software engineering skills, which can take years to teach. In this paper, we present an approach to introducing computing that allows students to safely and easily program high-performance robots. We develop a platform for students to program RoboCup Small Size League robots using JavaScript. The platform 1) ensures physical safety at several levels of abstraction, 2) allows students to program robots using the JavaScript in the browser, without the need to install software, and 3) presents a simplified JavaScript semantics that shields students from confusing language features. We discuss our experience running a week-long workshop using this platform, and analyze over 3,000 student-written program revisions to provide empirical evidence that our approach does help students.Comment: 8 pages, 7 figures, 4 table

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Task Switching and Single vs. Multiple Alarms for Supervisory Control of Multiple Robots

Foraging tasks, such as search and rescue or reconnaissance, in which UVs are either relatively sparse and unlikely to interfere with one another or employ automated path planning, form a broad class of applications in which multiple robots can be controlled sequen-tially in a round-robin fashion. Such human-robot systems can be described as a queuing sys-tem in which the human acts as a server while robots presenting requests for service are the jobs. The possibility of improving system performance through well-known scheduling tech-niques is an immediate consequence. Unfortunately, real human-multirobot systems are more complex often requiring operator monitoring and other ancillary tasks. Improving perfor-mance through scheduling (jobs) under these conditions requires minimizing the effort ex-pended monitoring and directing the operator’s attention to the robot offering the most gain. Two experiments investigating scheduling interventions are described. The first compared a system in which all anomalous robots were alarmed (Open-queue), one in which alarms were presented singly in the order in which they arrived (FIFO) and a Control condition without alarms. The second experiment employed failures of varying difficulty supporting an optimal shortest job first (SJF) policy. SJF, FIFO, and Open-queue conditions were compared. In both experiments performance in directed attention conditions was poorer than predicted. A possi-ble explanation based on effects of volition in task switching is propose

Optimizing Lead Time in Fall Detection for a Planar Bipedal Robot

For legged robots to operate in complex terrains, they must be robust to the disturbances and uncertainties they encounter. This paper contributes to enhancing robustness through the design of fall detection/prediction algorithms that will provide sufficient lead time for corrective motions to be taken. Falls can be caused by abrupt (fast-acting), incipient (slow-acting), or intermittent (non-continuous) faults. Early fall detection is a challenging task due to the masking effects of controllers (through their disturbance attenuation actions), the inverse relationship between lead time and false positive rates, and the temporal behavior of the faults/underlying factors. In this paper, we propose a fall detection algorithm that is capable of detecting both incipient and abrupt faults while maximizing lead time and meeting desired thresholds on the false positive and negative rates

Deep learning based approaches for imitation learning.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable