The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of study we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. Indeed this example paves the way to a variety of other similar applications.
For this reason, depth information has been exploited in a variety of robotics applications in the past @cite_16 @cite_11 @cite_1 @cite_5 @cite_8 @cite_9 . However, it is not easy to find methods for depth estimation from a stereo pair which are a good trade-off between robustness (e.g. to lighting conditions) and speed, two requirements that are key for working in real-world robotic scenarios. Therefore, alternative solutions as for example Kinect RGB-D sensors have been adopted, even when working on the iCub humanoid, which is equipped with a human-like stereo camera system (see @cite_19 ). The main motivation of this work is thus to upgrade'' the iCub robot's depth perception and to show that this improvement opens the way to a range of possible applications where disparity and depth can be successfully used to visually guide the robot's attention and actions also without relying on a Kinect.
@cite_8 @cite_9 @cite_1 @cite_19 @cite_5 @cite_16 @cite_11
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In this paper we present the current improvements of our biologically motivated interacting and learning vision system for humanoids. Building on the work presented by , 2005 the system features now a very natural gaze selection and interaction for learning freely presented complex objects in realtime. The new features are facilitated by two major contributions. First, by the introduction of an internal needs dynamics based on unspecific and specific rewards governing and exploring the parameterization of the basic behaviors. And second, by extending the object recognition pathway by sensory and object memory pathways as well as speech input output for interactive confirmation and object labeling In this paper, we present a unifying approach for learning and recognition of objects in unstructured environments through exploration. Taking inspiration from how young infants learn objects, we establish four principles for object learning. First, early object detection is based on an attention mechanism detecting salient parts in the scene. Second, motion of the object allows more accurate object localization. Next, acquiring multiple observations of the object through manipulation allows a more robust representation of the object. And last, object recognition benefits from a multi-modal representation. Using these principles, we developed a unifying method including visual attention, smooth pursuit of the object, and a multi-view and multi-modal object representation. Our results indicate the effectiveness of this approach and the improvement of the system when multiple observations are acquired from active object manipulation. We present a biologically motivated architecture for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The system combines biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. Training can be performed in an unconstrained environment by presenting objects in front of a stereo camera system and labeling them by speech input. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases. We demonstrate the performance on a challenging ensemble of 50 objects. We present a modular architecture for recognition and localization of objects in a scene that is motivated from coupling the ventral ("what") and dorsal ("where") pathways of human visual processing. Our main target is to demonstrate how online learning can be used to bootstrap the representation from nonspecific cues like stereo depth towards object-specific representations for recognition and detection. We show the realization of the system learning objects in a complex realworld environment and investigate its performance. This work is concerned with a framework for visual object recognition in real world tasks. Our approach is motivated by biological findings of the representation of space around the body, the so-called peripersonal space. We show that the principles behind those findings can lead to a natural structuring of object recognition tasks in artificial systems. We demonstrate this by the supervised learning and recognition of 20 complex-shaped objects from unsegmented visual input We present a biologically motivated system for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The training is unconstrained in the sense that arbitrary objects can be freely presented in front of a stereo camera system and labeled by speech input. The architecture unites biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases.