The Intelligent Object, short iObject+, is the second generation instrumented object, developed for research on human and robotic grasping and manipulation. It estimates its pose in space from an IMU sensor and measures interaction forces on its surface via an array of tactile sensors.
A major pre-requisite for many robotics tasks is to identify and localize objects within scenes. Our model-free approaches to scene segmentation employs RGBD cameras to segmented highly cluttered scenes in real-time (30 Hz). To this end, we first identify smooth object surfaces and subsequently combine them to form object hypotheses employing basic heuristics such has convexity, shape alignment and color similarity.
The Bielefeld CRC 673, Alignment in Communication, investigates special modes of coordination, called alignment. Alignment covers the adaptation processes among agents which are subconscious and do not lead to explicit negotiation and control of those engaged in a common enterprise. Alignment thus conceived can be observed in human-human communication with respect to the use of words, especially neologisms, the creation of new senses, copied patterns of syntax, recycled referring terms, the evolution of patterns in dialogue structure such as the use of ellipses and fragments.
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What principles enable rapid and adaptive alignment in coordination?
This project investigates Gestalt principles and their generalization from the perceptual into the action/cooperation domain for modeling adaptive alignment and its functional replication in human-robot cooperation. Departing from learning algorithms for dynamic Gestalt formation in layered recurrent networks (Competitive Layer Model CLM), we develop a hybrid, hierarchical architecture for adaptive alignment in cooperation that integrates elements from connectionist and symbol-based representations. We evaluate its performance in a human-robot cooperation scenario involving two anthropomorphic hands mounted on a bimanual robot platform.
Priming of relevant motor degrees of freedom to achieve rapid alignment of motor actions can be conceptualised as the rapid selection of low-dimensional action manifolds that capture the essential motor degrees of freedom. The present project investigates the construction of such manifolds from training data and how observed action trajectories can be decomposed into traversals of manifolds from a previously acquired repertoire. To this end we focus on manual actions of an anthropomorphic hand and combine Unsupervised Kernel Regression (UKR, a recent statistical learning method) with Competitive Layer Models (CLM, a recurrent neural network architecture) to solve the tasks of manifold construction and dynamic action segmentation.
The Intelligent Object, short iObject+, is the second generation instrumented object, developed for research on human and robotic grasping and manipulation. It estimates its pose in space from an IMU sensor and measures interaction forces on its surface via an array of tactile sensors.
A major pre-requisite for many robotics tasks is to identify and localize objects within scenes. Our model-free approaches to scene segmentation employs RGBD cameras to segmented highly cluttered scenes in real-time (30 Hz). To this end, we first identify smooth object surfaces and subsequently combine them to form object hypotheses employing basic heuristics such has convexity, shape alignment and color similarity.
What principles enable rapid and adaptive alignment in coordination?