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Kira,
Zsolt
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Zsolt
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ItemTransferring Embodied Concepts Between Perceptually Heterogeneous Robots(Georgia Institute of Technology, 2009) Kira, ZsoltThis paper explores methods and representations that allow two perceptually heterogeneous robots, each of which represents concepts via grounded properties, to transfer knowledge despite their differences. This is an important issue, as it will be increasingly important for robots to communicate and effectively share knowledge to speed up learning as they become more ubiquitous.We use Gӓrdenfors’ conceptual spaces to represent objects as a fuzzy combination of properties such as color and texture, where properties themselves are represented as Gaussian Mixture Models in a metric space. We then use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. These mappings are then used to transfer a concept from one robot to another, where the receiving robot was not previously trained on instances of the objects. We show in a 3D simulation environment that these models can be successfully learned and concepts can be transferred between a ground robot and an aerial quadrotor robot.
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ItemMapping Grounded Object Properties Across Perceptually Heterogeneous Embodiments(Georgia Institute of Technology, 2009) Kira, ZsoltAs robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences. Learning from experiences, however, is oftentimes embodiment-specific; that is, the knowledge learned is grounded in the robot’s unique sensors and actuators. This type of learning raises questions as to how communication and knowledge exchange via social interaction can occur, as properties of the world can be grounded differently in different robots. This is especially true when the robots are heterogeneous, with different sensors and perceptual features used to define the properties. In this paper, we present methods and representations that allow heterogeneous robots to learn grounded property representations, such as that of color categories, and then build models of their similarities and differences in order to map their respective representations. We use a conceptual space representation, where object properties are learned and represented as regions in a metric space, implemented via supervised learning of Gaussian Mixture Models. We then propose to use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. Results are demonstrated using two perceptually heterogeneous Pioneer robots, one with a web camera and another with a camcorder.
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ItemModeling Cross-Sensory and Sensorimotor Correlations to Detect and Localize Faults in Mobile Robots(Georgia Institute of Technology, 2007) Kira, ZsoltWe present a novel framework for learning crosssensory and sensorimotor correlations in order to detect and localize faults in mobile robots. Unlike traditional fault detection and identification schemes, we do not use a priori models of fault states or system dynamics. Instead, we utilize additional information and possible source of redundancy that mobile robots have available to them, namely a hierarchical graph representing stages of sensory processing at multiple levels of abstractions and their outputs. We learn statistical models of correlations between elements in the hierarchy, in addition to the control signals, and use this to detect and identify changes in the capabilities of the robot. The framework is instantiated using Self-Organizing Maps, a simple unsupervised learning algorithm. Results indicate that the system can detect sensory and motor faults in a mobile robot and identify their cause, without using a priori models of the robot or its fault states.
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ItemModeling Robot Differences by Leveraging a Physically Shared Context(Georgia Institute of Technology, 2007) Kira, Zsolt ; Long, KathrynKnowledge sharing, either implicit or explicit, is crucial during development as evidenced by many studies into the transfer of knowledge by teachers via gaze following and learning by imitation. In the future, the teacher of one robot may be a more experienced robot. There are many new difficulties, however, with regard to knowledge transfer among robots that develop embodiment-specific knowledge through individual solo interaction with the world. This is especially true for heterogeneous robots, where perceptual and motor capabilities may differ. In this paper, we propose to leverage similarity, in the form of a physically shared context, to learn models of the differences between two robots. The second contribution we make is to analyze the cost and accuracy of several methods for the establishment of the physically shared context with respect to such modeling. We demonstrate the efficacy of the proposed methods in a simulated domain involving shared attention of an object.
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ItemSpatio-Temporal Case-Based Reasoning for Efficient Reactive Robot Navigation(Georgia Institute of Technology, 2005) Likhachev, Maxim ; Kaess, Michael ; Kira, Zsolt ; Arkin, Ronald C.This paper presents an approach to automatic selection and modification of behavioral assemblage parameters for autonomous navigation tasks. The goal of this research is to make obsolete the task of manual configuration of behavioral parameters, which often requires significant knowledge of robot behavior and extensive experimentation, and to increase the efficiency of robot navigation by automatically choosing and fine-tuning the parameters that fit the robot task-environment well in real-time. The method is based on the Case-Based Reasoning paradigm. Derived from incoming sensor data, this approach computes spatial features of the environment. Based on the robot’s performance, temporal features of the environment are then computed. Both sets of features are then used to select and fine-tune a set of parameters for an active behavioral assemblage. By continuously monitoring the sensor data and performance of the robot, the method reselects these parameters as necessary. While a mapping from environmental features onto behavioral parameters, i.e., the cases, can be hard-coded, a method for learning new and optimizing existing cases is also presented. This completely automates the process of behavioral parameterization. The system was integrated within a hybrid robot architecture and extensively evaluated using simulations and indoor and outdoor real world robotic experiments in multiple environments and sensor modalities, clearly demonstrating the benefits of the approach.
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ItemForgetting Bad Behavior: Memory Management for Case-Based Navigation(Georgia Institute of Technology, 2004) Kira, Zsolt ; Arkin, Ronald C.In this paper, we present successful strategies for forgetting cases in a Case-Based Reasoning (CBR) system applied to autonomous robot navigation. This extends previous work that involved a CBR architecture which indexes cases by the spatio-temporal characteristics of the sensor data, and outputs or selects parameters of behaviors in a behavior-based robot architecture. In such a system, the removal of cases can be applied when a new situation unlike any current case in the library is encountered, but the library is full. Various strategies of determining which cases to remove are proposed, including metrics such as how frequently a case is used and a novel spreading activation mechanism. Experimental results show that such mechanisms can increase the performance of the system significantly and allow it to essentially forget old environments in which it was trained in favor of new environments it is currently encountering. The performance of this new system is better than both a purely reactive behavior-based system as well as the CBR module that did not forget cases. Furthermore, such forgetting mechanisms can be useful even when there is no major environmental shift during training, since some cases can potentially be harmful or rarely used. The relationship between the forgetting mechanism and the case library size is also discussed.