A Deep Incremental Boltzmann Machine for Modeling Context in Robots

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.Comment: 6 pages, 5 figures, International Conference on Robotics and Automation (ICRA 2018

Bir gezer robot sürüsünün bilgilendirilmiş robotlarla kontrol edilmesi.

In this thesis, we study how and to what extent a self-organized mobile robot flock can be guided by informing some of the robots within the flock about a preferred direction of motion. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space, avoiding obstacles. In its original form, this behavior does not have a preferred direction and the flock would wander aimlessly. In this study, we incorporate a preference for a goal direction in some of the robots. These informed robots do not signal that they are informed (a.k.a. unacknowledged leadership) and instead guide the swarm by their tendency to move in the desired direction. Through experimental results with physical and simulated robots we show that the self-organized flocking of a robot swarm can be effectively guided by an informed minority of the flock. We evaluate the system using a number of quantitative metrics: First, we propose to use the mutual information metric from Information Theory as a dynamical measure of the information exchange. Then, we discuss the accuracy metric from directional statistics and size of the largest cluster as the measures of system performance. Using these metrics, we perform analyses from two points of views: In the transient analyses, we demonstrate the information exchange between the robots as the time advances, and the increase in the accuracy of the flock when the conditions are suitable for an adequate amount of information exchange. In the steady state analyses, we investigate the interdependent effects of the size of the flock in terms of the robots in it, the ratio of informed robots in the flock over the total flock size, the weight of the direction preference behavior, and the noise in the system.M.S. - Master of Scienc

İnsansı robotlar için temellendirilmiş ve bağlamsal bir kavram ağı.

In this thesis, we propose a formalization for a densely connected representation of concepts and their contexts on a humanoid robot platform. Although concepts have been studied implicitly and explicitly in numerous studies before,our study is unique in placing the relatedness of concepts to the center: We hypothesize that a concept is fully meaningful only when considered in relation to the other concepts. Thus, we propose a novel densely connected web of concepts, and show how utilizing the relatedness of concepts can take cognition one step forward from the conventional approach that treats them individually. Then we use this densely connected framework for determining the context of encountered scenes. Although unanimously accepted as one of the pillars of cognition, our study is the first attempt to provide a dedicated and general formalization of context in a robotics setting. We follow a developmental approach in which the robot determines the existing contexts in its environment in an unsupervised manner, associates seen objects and whole scenes with these contexts as appropriate, and further utilizes this extracted contextual information in reasoning and planning. As required by the developmental paradigm, the programmer’s input to the robot in terms of informational bias is kept at a minimum, and the robot is expected to deduce the important characteristics of the environment itself, such as the number of contexts hidden in its environment, if and when to introduce another context to its world model, and how these contexts probabilistically give rise to the related concepts in this world.Ph.D. - Doctoral Progra

Recurrent Slow Feature Analysis for Developing Object Permanence in Robots

In this work, we propose a biologically inspired framework for developing object permanence in robots. In particular, we build upon a previous work on a slowness principle-based visual model (Wiskott and Sejnowski, 2002), which was shown to be adept at tracking salient changes in the environment, while seamlessly “understanding” external causes, and self-emerging structures that resemble the human visual system. We propose an extension to this architecture with a prefrontal cortex-inspired recurrent loop that enables a simple short term memory, allowing the previously reactive system to retain information through time. We argue that object permanence in humans develop in a similar manner, that is, on top a previously matured object concept. Furthermore, we show that the resulting system displays the very behaviors which are thought to be cornerstones of object permanence understanding in humans. Specifically, the system is able to retain knowledge of a hidden object’s velocity, as well as identity, through (finite) occluded periods

A Probabilistic Concept Web on a Humanoid Robot

It is now widely accepted that concepts and conceptualization are key elements towards achieving cognition on a humanoid robot. An important problem on this path is the grounded representation of individual concepts and the relationships between them. In this article, we propose a probabilistic method based on Markov Random Fields to model a concept web on a humanoid robot where individual concepts and the relations between them are captured. In this web, each individual concept is represented using a prototype-based conceptualization method that we proposed in our earlier work. Relations between concepts are linked to the cooccurrences of concepts in interactions. By conveying input from perception, action, and language, the concept web forms rich, structured, grounded information about objects, their affordances, words, etc. We demonstrate that, given an interaction, a word, or the perceptual information from an object, the corresponding concepts in the web are activated, much the same way as they are in humans. Moreover, we show that the robot can use these activations in its concept web for several tasks to disambiguate its understanding of the scene

Learning to Increment A Contextual Model

In this paper, we summarized our efforts on incremental construction of latent variables in context (topic) models. With our models, an agent can incrementally learn a representation of critical contextual information. We demonstrated that a learning-based formulation outperforms rule-based models, and generalizes well across many settings and to real dat

Self-organized flocking in mobile robot swarms

In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots. We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a “super-organism”. We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication. Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics

Self-organized flocking with a mobile robot swarm

This paper studies self-organized flocking in a swarm of mo- bile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies, briefly de- scribing its sensing and communication abilities. In particular, we describe a scalable method that allows the robots to sense the orientations of their neighbors using a digital compass and wireless communication. Then we propose a behavior for a swarm of robots that creates self-organized flocking by using heading alignment and proximal control. The flocking behavior is observed to operate in three phases: alignment, advance, and avoidance. We evaluate four variants of this behavior by setting its parameters to extreme values and analyze the performance of flocking using a number of metrics, such as order and entropy. Our results show that, the flocking behavior obtained under appropriate parameter values, is quite robust and generates successful self- organized flocking in constraint environments

Oğul Robot Sistemleri için Basit Bir Görüntüleme Sistemi Tasarımı

Bu bildiride, oğul robot sistemleri için tasarlanmış¸ gezer robotların üzerine yerleştirilmiş küresel bir ayna ve bu aynayı gören bir kamera yardımıyla 360 derecelik görüntü verisi almaya dayanan bir görüntüleme sistemi anlatılmıştır. Oğul robotların davranışlarında kararlı olabilmeleri, görüntüleme sisteminin hızlı ve kararlı bir biçimde çevreyi algılayabilmesine bağlıdır. Sonuçlar sistemin kendinden beklenen hıza ulaşabildiğini (saniyede 12 güncelleme) ve çevrenin anlık değişimlerinden olumsuz etkilenmediğini göstermektedir. Görüntüleme sisteminin oğul robotların kümelenme davranışı sırasındaki başarısı ve bunu sağlayan etmenler tartışılmıştır