Neuroinformatics Group

Universität BielefeldTechnische FakultätNI

Research Projects

NEATfields: Evolution of large neural networks

In the last decades, many researchers have used evolutionary algorithms to adapt the topology and connection weights of recurrent neural networks for various control tasks. This has become a useful machine learning technique. Because handling large genomes is difficult, however, these neural networks typically contain only a few neurons. If the genome contains a recipe for construction of the network instead of the network itself, it can be much smaller. We have developed a method than can exactly do this, and performs very well on a number of different problems.

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Adaptive alignment in human-robot-cooperation

Alignment is not restricted to support language, dialog functions and the execution of speech production. Similar mechanisms support the cooperative execution of more general actions. Besides the extension of alignment into the action domain, we hypothese that the formation of alignment is adaptive: Repetitions of actions facilitate the alignment and its adaptation is important for acquiring team expertise. In our opinion, adaptation and adaptive alignment paves the way for smooth and effective common acting.

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Robots Exploring their Bodies Autonomously (REBA)

Motivated by the need of complex anthropomorphic robots to manage sophisticated spatial relationships between parts of their body and the environment, together with recent findings from the neurosciences about how the brain solves this challenge by means of a highly adaptable “body schema”, the present proposal pursues the goal of
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Imitation Learning

Human Assistance ScenarioIn the near future, more and more people will need assistance in everyday tasks while they still want to maintain a high degree of self-reliance. Cognitive robot servants will fulfill their individual needs. One promising way develop robots with a sufficiently high adaptability is to equip cognitive robots with task learning abilities, that lets them learn a task from demonstrations of naive (non-expert) users. This paradigm is widely known as Programming by Demonstration (PbD) or Imitation Learning.

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Learning Control Behaviour within the Control Basis Framework

The Control Basis Framework (by Grupen et. al. 1998) is a powerful approach to closed loop control. This project aims at providing a library implementing the Control Basis Framework idea and possibly extending it to concurrent execution. Additional research is planned to investigate how to make machines learn to utilize the control affordances provided by synthesized controllers. read more »

Manipulating Paper

Manipulation of paper is a rich domain of manual intelligence that we encounter in many daily tasks. The present project attempts to analyse and implement the "web" of visuo-motor coordination skills to endow an anthropomorphic robot hand with the ability to manipulate paper (and paper-like objects) in a variety of situations of increasing complexity. This will include aspects such as modeling interaction with compliant objects, action based representation as well as bimanual coordination to enable object transformations such as tearing and folding.

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Vision-based Grasping

Unlike most existing approaches to the grasp selection task for anthropomorphic robot hands, this vision-based project aims for a solution, which does not depend on an a-priori known 3D shape of the object. Instead it uses a decomposition of the object view (obtained from mono or stereo cameras) into local, grasping-relevant shape primitives, whose optimal grasp type and approach direction are known or learned beforehand. Based on this decomposition a list of possible grasps can be generated and ordered according to the anticipated overall grasp quality.

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Autonomous Exploration of Manual Interaction Space

We gradually increase our manual competence by exploring manual interaction spaces for many different kinds of objects. This is an active process that is very different from passive perception of "samples". The availability of humanoid robot hands offers the opportunity to investigate different strategies for such active exploration in realistic settings. In the present project, the investigation of such strategies shall be pursued from the perspective of „multimodal proprioception:“ correlating joint angles, partial contact information from touch sensors and joint torques as well as visual information about changes in finger and object position in such a way as to make predictions about "useful aspects" for shaping the ongoing interaction.

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SARAFun: Smart Assembly Robots

The SARAFun project has been formed to enable a non-expert user to integrate a new bi-manual assembly task on a robot in less than a day. This will be accomplished by augmenting the robot with cutting edge sensory and cognitive abilities as well as reasoning abilities required to plan and execute an assembly task.

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Shadow Hand Tactile Fingertip

This 12-taxel fingertip sensor provides highly sensitive normal-force sensing for our Shadow Robot Hands - fully integrated with the mechanical and electrical design of the hand.

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TACT-HAND: Improving control of prosthetic hands using tactile sensors and machine learning

Computer aided rendering of tactile sensor units fitting onto the I-limb Ultra prosthetic hand Despite decades of research, intuitive and robust control of multi-joint prosthetic hands is still an unsolved problem, largely due to missing sensorization of the hand and a poor human-machine interface, that only barely can recognize the intent of the patient. In this project we will employ and evaluate a new generation of tactile sensors coupled with modern machine learning approaches to overcome both problems.

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Human grasping forces and positions synergies

Because of the complex anatomy of the human hand, in the absence of external constraints a large number of postures and force combinations can be used to attain a stable grasp.

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WearHap: Wearable haptics for Humans and Robots

Tactile sensitive datagloveWithin the EU-Project WearHap, we develop different wearable devices, most notably a tactile-sensing glove, based on our elastic, fabric-based tactile-sensor-technology. The Tactile Glove provides normal force/pressure measurement in approx 60 taxels spread across the whole palmar side of the hand and allows to record human interaction forces while grasping or manipulating objects.

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Intelligent Object (iObject+)

iObjectPlus 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.

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Sim4Dexterity

logoZiel des vom BMBF geförderten Verbundvorhabens Sim4Dexterity ist die Erzeugung synthetischer Daten für die Robotermanipulation mit hochwertigen, interaktiven und validierbaren Simulationswerkzeugen, so dass benötigte KI-Lösungen wirtschaftlich und zeiteffizient realisiert werden können.

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NeuTouch

The NeuTouch ITN aims at improving artificial tactile systems, by training a new generation of researchers that study how human and animal’s tactile systems work, develop a new type of technology that is based on the same principles, and use this technology for building robots that can help humans in daily tasks and artific

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Fingernail with static and dynamic force sensing

ActiveFingernailThe human fingernail turns out to be very important when it comes to picking coins, scratching off stickers, or simply probing surfaces. To endow robots with similar capabilities, in this project, we are developing a sensorized fingernail for mechanical hands, i.e. robotic or prostetic hands, capable of measuring vibratory and static contact forces. High sensitivity combined with robustness for daily use are the key features describing our proposed design.

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