In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: (1) imitation is primarily directed toward reproducing the outcome of an observed action sequence rather than reproducing the exact action means, and (2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing appropriate goal-directed sequences of motor primitives. The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints.
We propose a robust methodology for 3D modelbased markerless tracking of textured objects in monocular image sequences. The technique is based on mutual information maximization, a widely known criterion for multimodal image registration, and employs an efficient multiresolution strategy in order to achieve robustness while keeping fast computational time, thus achieving near real-time performance for visual tracking of complex textured surfaces.
Abstract-In this paper, we present a system for controlling a quadrocopter using both optical and inertial measurements. We show how to use external stereo camera measurements for visual servoing, by onboard fusion at high rates, only natural features provided by the vehicle and without any active marker.In our experiments, we show the accuracy and robustness of our system during indoor flights, as well as robustness to external flight disturbances.
In this paper we present an efficient and robust real-time system for object contour tracking in image sequences. The developed application partly relies on an optimized implementation of a state-of-the-art curve fitting algorithm, and integrates important additional features in order to achieve robustness while keeping the speed of the main estimation algorithm. An application program has been developed, which requires only a few standard libraries available on most platforms, and runs at video frame rate on a common PC with standard hardware equipment. Motivation and Scope of the Present WorkThe general problem of object contour tracking in image sequences is an important and challenging topic in the computer vision community; as many researchers already pointed out, an advanced contour tracking technique can provide crucial information for many image understanding problems and, at the same time, allows the development of efficient and useful working applications in many fields of interest. We refer the reader to [1] for a survey of these applications and the related references.Among the currently available methodologies, a very appealing one is the Contracting Curve Density (CCD) algorithm: this method has been recently developed and presented in [3] as a state-of-the-art improvement over other advanced techniques such as the Condensation algorithm [6], and it has been shown to overperform them in many different estimation tasks. Nevertheless, its higher complexity has been initially considered as an obstacle to an effective real-time implementation, even in the simplified form named Real-Time CCD from the same authors [4], and a working online version still had to be investigated.Moreover, in order to realize an autonomous and robust tracking system, some important additional issues have to be taken into account; one of them is the possibility of automatic initialization and reinitialization of the system, both at the beginning of the tracking, and in case of tracking loss. Nevertheless, one also expects that a "well behaving" tracking system does not need a global estimation procedure during most of the tracking task, or, in the ideal case, only at the beginning. The global initialization module is computationally more demanding than the online tracker, and a frequent reinitialization would significantly slow down the performance of the system. Therefore, most of the efforts in realizing such a system have to be focused on a robust real-time performance of the contour tracker itself, in terms of speed, accuracy and convergence area for the parameter search.All of the above mentioned issues, and other relevant aspects, constitute the main motivation of the present work; it will be shown how it contributes to the state-of-the-art in contour tracking systems with the following achievements:• The real-time CCD algorithm [4] has been reimplemented with a significant number of critical speedups with respect to the originally proposed version.• A global initialization and reinitialization module has been developed ...
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