Baran Çürüklü

Robots: ethical by design

Among ethicists and engineers within robotics there is an ongoing discussion as to whether ethical robots are possible or even desirable. We answer both of these questions in the positive, based on an extensive literature study of existing arguments. Our contribution consists in bringing together and reinterpreting pieces of information from a variety of sources. One of the conclusions drawn is that artifactual morality must come in degrees and depend on the level of agency, autonomy and intelligence of the machine. Moral concerns for agents such as intelligent search machines are relatively simple, while highly intelligent and autonomous artifacts with significant impact and complex modes of agency must be equipped with more advanced ethical capabilities. Systems like cognitive robots are being developed that are expected to become part of our everyday lives in future decades. Thus, it is necessary to ensure that their behaviour is adequate. In an analogy with artificial intelligence, which is the ability of a machine to perform activities that would require intelligence in humans, artificial morality is considered to be the ability of a machine to perform activities that would require morality in humans. The capacity for artificial (artifactual) morality, such as artifactual agency, artifactual responsibility, artificial intentions, artificial (synthetic) emotions, etc., come in varying degrees and depend on the type of agent. As an illustration, we address the assurance of safety in modern High Reliability Organizations through responsibility distribution. In the same way that the concept of agency is generalized in the case of artificial agents, the concept of moral agency, including responsibility, is generalized too. We propose to look at artificial moral agents as having functional responsibilities within a network of distributed responsibilities in a socio-technological system. This does not take away the responsibilities of the other stakeholders in the system, but facilitates an understanding and regulation of such networks. It should be pointed out that the process of development must assume an evolutionary form with a number of iterations because the emergent properties of artifacts must be tested in real world situations with agents of increasing intelligence and moral competence. We see this paper as a contribution to the macro-level Requirement Engineering through discussion and analysis of general requirements for design of ethical robots.

Intuitive industrial robot programming through incremental multimodal language and augmented reality

Developing easy to use, intuitive interfaces is crucial to introduce robotic automation to many small medium sized enterprises (SMEs). Due to their continuously changing product lines, reprogramming costs exceed installation costs by a large margin. In addition, traditional programming methods for industrial robots is too complex for an inexperienced robot programmer, thus external assistance is often needed. In this paper a new incremental multimodal language, which uses augmented reality (AR) environment, is presented. The proposed language architecture makes it possible to manipulate, pick or place the objects in the scene. This approach shifts the focus of industrial robot programming from coordinate based programming paradigm, to object based programming scheme. This makes it possible for non-experts to program the robot in an intuitive way, without going through rigorous training in robot programming.

An abstract model of a cortical hypercolumn

An abstract model of a cortical hypercolumn is presented. This model could replicate experimental findings relating to the orientation tuning mechanism in the primary visual cortex. Properties of the orientation selective cells in the primary visual cortex, like contrast-invariance and response saturation, were demonstrated in simulations. We hypothesize that broadly tuned inhibition and local excitatory connections are sufficient for achieving this behavior. We have shown that the local intracortical connectivity of the model is to some extent biologically plausible.

Object selection using a spatial language for flexible assembly

In this paper we present a new simplified natural language that makes use of spatial relations between the objects in scene to navigate an industrial robot for simple pick and place applications. Developing easy to use, intuitive interfaces is crucial to introduce robotic automation to many small medium sized enterprises (SMEs). Due to their continuously changing product lines, reprogramming costs are far more higher than installation costs. In order to hide the complexities of robot programming we propose a natural language where the use can control and jog the robot based on reference objects in the scene. We used Gaussian kernels to represent spatial regions, such as left or above. Finally we present some dialogues between the user and robot to demonstrate the usefulness of the proposed system.

A general framework for incremental processing of multimodal inputs

Humans employ different information channels (modalities) such as speech, pictures and gestures in their communication. It is believed that some of these modalities are more error-prone to some specific type of data and therefore multimodality can help to reduce ambiguities in the interaction. There have been numerous efforts in implementing multimodal interfaces for computers and robots. Yet, there is no general standard framework for developing them. In this paper we propose a general framework for implementing multimodal interfaces. It is designed to perform natural language understanding, multi- modal integration and semantic analysis with an incremental pipeline and includes a multimodal grammar language, which is used for multimodal presentation and semantic meaning generation.

Real-World Data Collection with UYANIK

In this chapter, we present data collection activities and preliminary research findings from the real-world database collected with “UYANIK,” a passenger car instrumented with several sensors, CAN-Bus data logger, cameras, microphones, data acquisitions systems, computers, and support systems. Within the shared frameworks of Drive-Safe Consortium (Turkey) and the NEDO (Japan) International Collaborative Research on Driving Behavior Signal Processing, close to 16 TB of driver behavior, vehicular, and road data have been collected from more than 100 drivers on a 25 km route consisting of both city roads and The Trans-European Motorway (TEM) in Istanbul, Turkey. Challenge of collecting data in a metropolis with around 12 million people and famous with extremely limited infrastructure yet driving behavior defying all rules and regulations bordering madness could not be “painless.” Both the experience gained and the preliminary results from still on-going studies using the database are very encouraging and give comfort.

Towards robust human robot collaboration in industrial environments

In this paper a system, which is driven through natural language, that allows operators to select and manipulate objects in the environment using an industrial robot is proposed. In order to hide the complexities of robot programming we propose a natural language where the user can control and jog the robot based on reference objects in the scene. We used semantic networks to relate different types of objects in the scene.

Gesture recognition using evolution strategy neural network

A new approach to interact with an industrial robot using hand gestures is presented. System proposed here can learn a first time userpsilas hand gestures rapidly. This improves product usability and acceptability. Artificial neural networks trained with the evolution strategy technique are found to be suited for this problem. The gesture recognition system is an integrated part of a larger project for addressing intelligent human-robot interaction using a novel multi-modal paradigm. The goal of the overall project is to address complexity issues related to robot programming by providing a multi-modal user friendly interacting system that can be used by SMEs.

A model of the summation pools within the layer 4 (area 17)

We propose a developmental model of the summation pools within the layer 4. The model is based on the modular structure of the neocortex and captures some of the known properties of layer 4. Connections between the orientation minicolumns are developed during exposure to visual input. Excitatory local connections are dense and biased towards the iso-orientation domain. Excitatory long-range connections are sparse and target all orientation domains equally. Inhibition is local. The summation pools are elongated along the orientation axis. These summation pools can facilitate weak and poorly tuned LGN input and explain improved visibility as an effect of enlargement of a stimulus.

Intensity normalization of sidescan sonar imagery

Sonar imaging is currently the exemplary choice used in underwater imaging. However, since sound signals are absorbed by water, an image acquired by a sonar will have gradient illumination; thus, underwater maps will be difficult to process. In this work, we investigated this phenomenon with the objective to propose methods to normalize the images with regard to illumination. We propose to use MIxed exponential Regression Analysis (MIRA) estimated from each image that requires normalization. Two sidescan sonars have been used to capture the seabed in Lake Vattern in Sweden in two opposite directions west-east and east-west; hence, the task is extremely difficult due to differences in the acoustic shadows. Using the structural similarity index, we performed similarity analyses between corresponding regions extracted from the sonar images. Results showed that MIRA has superior normalization performance. This work has been carried out as part of the SWARMs project (http://www.swarms.eu/).