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Localization and manipulation of features such as buttons, snaps, or grommets embedded in fabrics and other flexible materials is a difficult robotics problem. Approaches that rely too much on sensing and localization that occurs before touching the material are likely to fail because the flexible material can move when the robot actually makes contact. This paper experimentally explores the possibility...
The interaction experiment, between a robot and a rat, will benefit significantly when the rat's actions can be recognized automatically in real time. Regarding quantitative behavior analysis, the number and duration of a rat's actions should be measured efficiently and accurately. Therefore, aiming at the above-mentioned objectives, a novel cognition system capable of detecting rats' actions has...
Novelty detection would be a useful ability for any autonomous robot that seeks to categorize a new environment or notice unexpected changes in its present one. A biomimetic robot (SCRATCHbot) inspired by the rat whisker system was here used to examine the performance of a novelty detection algorithm based on a “naive” implementation of Bayes rule. Naive Bayes algorithms are known to be both efficient...
In this paper, we propose a two-modes autonomous controller for a snake-like robot to adapt to the environments with different friction coefficients. According to the cyclic 3-stages experiential learning theory, the controller integrates an open-form searching method and a closed-form searching method, which correspond to the two modes of the controller respectively. We introduce a mechanism to remember...
In recent years, virtual reality has emerged as a key technology for improving and streamlining design, programming, manufacturing and training processes. Based on experiences in the fields of space robotics, industrial manufacturing, multi-physics and virtual prototyping, "virtual testbeds" are currently being designed and implemented. Building on experiences gained in space robotics applications,...
One of the major obstacles that hinders the application of robots to human day-to-day tasks is the current lack of flexible learning methods to endow the robots with the necessary skills and to allow them to adapt to new situations. In this work, we present a new intuitive method for teaching a robot anthropomorphic motion primitives. Our method combines the advantages of reinforcement and imitation...
This paper describes a pedestrian detection method using a LRF and a small omni-view camera. In outdoor environment, the resolutions of LRFs are too low to recognize human reliably, and high resolution image requires high calculation cost for detecting walking persons. We propose a combination approach using these data. Particle filter based tracking and HOG (Histogram of Oriented Gradients) feature...
This paper describes a framework for an interactive robot-based tutoring system (IRTS). The proposed IRTS is based on combining aspects of intelligent tutoring systems (ITSs) which are computer-based expert systems to simulate aspects of a human tutor, and robot-assisted systems which help users with physical interaction using robotic devices. The IRTS involves the robot interacting with a user, generating...
One of the major challenges in developing autonomous systems is to make them able to recognize and categorize objects robustly. However, the appearance-based algorithms that are widely employed for robot perception do not explore the functionality of objects, described in terms of their affordances. These affordances (e.g., manipulation, grasping) are discriminative for object categories and are important...
Accurate, efficient and robust location recognition is a fundamental task for any mobile robot. This paper presents a new approach using visual features to efficiently represent a series of locations along a path in an indoor environment. In the training stage, local features which are detected across multiple images from a single tour are combined to represent a real-world landmark, modelled by the...
The position of a sound source is an important information for robotic systems to be extracted from a sound. Of the three spherical coordinates (azimuth, elevation, distance) only the azimuth direction is extracted in most robot audition systems. So far rarely investigated is the issue of estimating the distance between robot and sound source. In this article we describe a study on distance estimation...
An appearance-based similarity measure for localizing a robot along a route is presented. This measure assesses the likelihood that the robot lies between a pair of positions where snapshot images were captured during training. The change in the scale parameter of matched SIFT features is used to determined whether the robot lies ahead or behind each snapshot. Experimental results in two different...
An adaptive partition based Random Forests classifier for outdoor terrain classification is presented in this paper. The classifier is a combination of two underlying classifiers. One of which is a random forest learnt over bootstrapped or offline dataset, the second is another random forest that adapts to changes on the fly. Posterior probabilities of both the static and changing/online classifiers...
This paper proposes an incremental scheme for visual landmark learning and recognition. The feature selection stage characterises the landmark using the Opponent SIFT, a color-based variant of the SIFT descriptor. To reduce the dimensionality of this descriptor, an incremental non-parametric discriminant analysis is conducted to seek directions for efficient discrimination (incremental eigenspace...
This paper describes an approach for mobile robot localization using a visual word based place recognition approach. In our approach we exploit the benefits of a stereo camera system for place recognition. Visual words computed from SIFT features are combined with VIP (viewpoint invariant patches) features that use depth information from the stereo setup. The approach was evaluated under the ImageCLEF@ICPR...
Historically, learning algorithms have been applied to games as a test of their performance, and with the exponential increases in available computational power, machine learning has been attempted in increasingly complex environments. This paper details the application of neuroevolution of augmenting topologies (NEAT) and accuracy-based learning classifier system (XCS) to the Robocode game environment,...
Collision detection and three-dimensional modeling are two important technologies in robot virtual reality system. Technique of three-dimensional modeling based on Creator software is introduced in this paper. On the basis of it, it creates bomb-disposal robot simulation model, etc. In scene simulation, collision detection could improve locomotion reality and avoid the phenomena of mutual penetration...
Although robot navigation in indoor environments has achieved great success, robots are unable to fully navigate these spaces without the ability to operate elevators, including those which the robot has not seen before. In this paper, we focus on the key challenge of autonomous interaction with an unknown elevator button panel. A number of factors, such as lack of useful 3D features, variety of elevator...
Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying safe, traversable paths that allow the robot to progress towards a goal while avoiding obstacles. Machine learning techniques are well adapted to this task, accomplishing near-to-far learning by training appearance-based models using...
Novelty detection is often treated as a one-class classification problem: how to segment a data set of examples from everything else that would be considered novel or abnormal. Almost all existing novelty detection techniques, however, suffer from diminished performance when the number of less relevant, redundant or noisy features increases, as often the case with high-dimensional feature spaces....
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