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In this paper we report the results of our research on learning and developing cognitive systems. The results are integrated into ALIS 3, our Autonomous Learning and Interacting System version 3 realized the humanoid robot ASIMO. The results presented address crucial issues in autonomously acquiring mental concepts in artifacts. The major contributions are the following: We researched distributed...
Man-made real-world environments are dominated by planar surfaces many of which constitute behavior-relevant entities. Thus, the ability to perceive planar surfaces is vital for any embodied system operating in such environments, be it human or robotic. In this paper, we present an architecture for detection and estimation of planar surfaces in the scene from calibrated stereo images. They are represented...
Based on inspirations from infant development we present a system which learns associations between acoustic labels and visual representations in interaction with its tutor. The system is integrated with a humanoid robot. Except for a few trigger phrases to start learning all acoustical representations are learned online and in interaction. Similar, for the visual domain the clusters are not predefined...
A stable perception of the environment is a crucial prerequisite for researching the learning of semantics from human-robot interaction and also for the generation of behavior relying on the robots perception. In this paper, we propose several contributions to this research field. To organize visual perception the concept of proto-objects is used for the representation of scene elements. These proto-objects...
We introduce our latest autonomous learning and interaction system instance ALIS 2. It comprises different sensing modalities for visual (depth blobs, planar surfaces, motion) and auditory (speech, localization) signals and self-collision free behavior generation on the robot ASIMO. The system design emphasizes the split into a completely autonomous reactive layer and an expectation generation layer...
The contribution of this paper is twofold. First, we present a new conceptual framework for modeling incremental hierarchical behavior control systems for humanoids. The biological motivation and the key elements are discussed. Second, we show our current instance of such a behavior control system, called ALIS. It is designed according to the concepts presented within the framework. The system is...
We describe a system for visual interaction developed for humanoid robots. It enables the robot to interact with its environment using a smooth whole body motion control driven by stabilized visual targets. Targets are defined as visually extracted "proto-objects" and behavior-relevant object hypotheses and are stabilized by means of a short-term sensory memory. Selection mechanisms are...
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