Dr. Axenie has been invited to hold a lecture at the Munich School of Engineering of TU Munich on the 17.06.2019. Research from AKII Microlab was introduced to undergrad students of TUM. Thank you TUM Graduate School for the invitation.
Dr. Axenie together with two students of THI (team NeuroTHIx: Du Xiaorui, Yavuzhan Erdem, Cristian Axenie) qualified for the final of Merck “Future of AI” Challenge.
IRENA (Invariant Representations Extraction in Neural Architectures)
In this project we aim at building an unsupervised learning system that is based on and
inspired by our biological intelligence for the problem of learning invariant representations.
Mammalian visual systems are characterized by their ability to recognize stimuli invariant to
various transformations. With our proposed model, we investigate the hypothesis that this
ability is achieved by the temporal encoding of visual stimuli, and why not, other sensory
stimuli. By using a model of a multisensory cortically inspired network, we show that this
encoding is invariant to several transformations and robust with respect to stimulus
variability. Furthermore, we show that the proposed model provides a rapid encoding and
computation, in accordance with recent physiological results.
https://app.ekipa.de/challenge/future-of-ai/brief
The team is now invited in Darmstadt at Merck’s Research Center for a 2 day boot camp to refine the idea and then work on it until end August. To conclude the Challenge, we will present our final solution in Darmstadt on 30th of August.
Well done and good luck!
The International Conference on Artificial Neural Networks (ICANN) is the annual flagship conference of the European Neural Network Society (ENNS). For the 2019 edition of ICANN AKII Microlab has two papers accepted.
Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality – Sebastian Pohl, Armin Becher, Thomas Grauschopf, Cristian Axenie
NARPCA: Neural Accumulate-Retract PCA for Low-latency High-throughput Processing on Datastreams – Cristian Axenie, Radu Tudoran, Stefano Bortoli, Mohamad Al Hajj Hassan, Goetz Brasche
Well done team !
Vortrag: Im Reich der Zeichen: Geheime Botschaften – Entstehung und Entwicklung chinesischer Schriftzeichen
Die chinesische Schrift reflektiert sprachliche sowie auch kulturelle Eigenheiten Chinas, sagt unsere Referentin, Frau Esther Haubensack, Sinologin und Linguistin. An dem Vortragabend möchte sie mit den Besuchern (keine Chinesisch-Vorkenntnisse erforderlich!) anhand einiger Beispiele deren Entstehung, Aufbau und Entwicklung verfolgen. Die Enthüllung der Geheimnisse verhilft dann sogar, einzelne Zeichen bestimmten Bedeutungsfeldern zuordnen zu können.
Zur Referentin:
Esther Haubensack, M.A. studierte an der Ludwig-Maximilians-Universität München Linguistik und Sinologie. Während ihres Auslandsstudiums in Peking führte sie ihre Sprachbegeisterung auf die chinesische Fernsehbühne, wo sie in Sketchen und Fernsehserien mitwirkte. Derzeit pendelt sie als mehrsprachige Moderatorin zwischen China und Deutschland.
In a rather small group, increased by a spontaneous KU Eichstätt student delegation visit, the AKII Microlab Xmas party was a warm and really “sweet event”. Thanks to Thomas we enjoyed all the season traditional German sweets and Glühwein. The afternoon brought interesting chats and offered ground for new research challenges in 2019.
Merry Christmas and a Happy New Year!
After a successful acceptance of our first paper on VIRTOOAIR, Armin has been invited to present and demo AKII Microlab’s flagship project at the IEEE ISMAR 2018. This is the leading international academic conference in the fields of Augmented Reality and Mixed Reality. The symposium is organized and supported by the IEEE Computer Society, IEEE VGTC, and ACM SIGCHI. Good job Armin!
Dr. Axenie’s latest work in Online Machine Learning for Big Data Streaming, introducing STARLORD: Sliding window Temporal Accumulate-Retract Learning for Online Reasoning on Datastreams, has been accepted in the Machine Learning Algorithms, Systems and Applications track of the IEEE 17th International Conference on Machine Learning and Applications ICMLA 2018.
Congratulations Cristian and the Huawei GRC team !
14.09.2018 – Dr. Axenie was invited to hold a lecture in Lions Club Ubersee – Cyber. Along with Dennis Morgenstern, Head of Google Automotive, Dr. Axenie introduced to the entrepreneurial audience the values AI and VR have for the digital transformation.
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A series of collaborations with VW and Audi which want to collaborate in VR. Work investigating high-end renderings, construction validation, virtual installations and ergonomics. We focused on analyzing latencies in distributed VR.
Efficient sensorimotor processing is inherently driven by physical real-world constraints that an acting agent faces in its environment. Sensory streams contain certain statistical dependencies determined by the structure of the world, which impose constraints on a system’s sensorimotor affordances.
This limits the number of possible sensory information patterns and plausible motor actions. Learning mechanisms allow the system to extract the underlying correlations in sensorimotor streams.
This research direction focused on the exploration of sensorimotor learning paradigms for embedding adaptive behaviors in robotic system and demonstrate flexible control systems using neuromorphic hardware and neural-based adaptive control. I employed large-scale neural networks for gathering and processing complex sensory information, learning sensorimotor contingencies, and providing adaptive responses.
To investigate the properties of such systems, I developed flexible embodied robot platforms and integrate them within a rich tool suite for specifying neural algorithms that can be implemented in embedded neuromorphic hardware.
The mobile manipulator I developed at NST for adaptive sensorimotor systems consists of an omni-directional (holonomic) mobile manipulation platform with embedded low-level motor control and multimodal sensors.
The on-board micro-controller receives desired commands via WiFi and continuously adapts the platform’s velocity controller. The robot’s integrated sensors include wheel encoders for estimating odometry, a 9DoF inertial measurement unit, a proximity bump-sensor ring and three event-based embedded dynamic vision sensors (eDVS) for visual input.
The mobile platform carries an optional 6 axis robotic arm with a reach of >40cm. This robotic arm is composed of a set of links connected together by revolute joints and allows lifting objects of up to 800 grams. The mobile platform contains an on-board battery of 360 Wh, which allows autonomous operation for well above 5h.
My research interest is in developing sensor fusion mechanisms for robotic applications. In order to extend the framework of the interacting areas a second direction in my research focuses on learning and development mechanisms.
Human perception improves through exposure to the environment. A wealth of sensory streams which provide a rich experience continuously refine the internal representations of the environment and own state. Furthermore, these representations determine more precise motor planning.
An essential component in motor planning and navigation, in both real and artificial systems, is egomotion estimation. Given the multimodal nature of the sensory cues, learning crossmodal correlations improves the precision and flexibility of motion estimates.
During development, the biological nervous system must constantly combine various sources of information and moreover track and anticipate changes in one or more of the cues. Furthermore, the adaptive development of the functional organisation of the cortical areas seems to depend strongly on the available sensory inputs, which gradually sharpen their response, given the constraints imposed by the cross-sensory relations.
Learning processes which take place during the development of a biological nervous system enable it to extract mappings between external stimuli and its internal state. Precise ego-motion estimation is essential to keep these external and internal cues coherent given the rich multisensory environment. In this work we present a learning model which, given various sensory inputs, converges to a state providing a coherent representation of the sensory space and the cross-sensory relations.
The model is based on Self-Organizing-Maps and Hebbian learning (see Figure 1) using sparse population coded representations of sensory data. The SOM is used to represent the sensory data, while the Hebbian linkage extracts the coactivation pattern given the input modalities eliciting peaks of activity in the neural populations. The model was able to learn the intrinsic sensory data statistics without any prior knowledge (see Figure 2).
The developed model, implemented for 3D egomotion estimation on a quadrotor, provides precise estimates for roll, pitch and yaw angles (setup depicted in Figure 3a, b).
Given relatively complex and multimodal scenarios in which robotic systems operate, with noisy and partially observable environment features, the capability to precisely and timely extract estimates of egomotion critically influences the set of possible actions.
Utilizing simple and computationally effective mechanisms, the proposed model is able to learn the intrinsic correlational structure of sensory data and provide more precise estimates of egomotion (see Figure 4a, b).
Moreover, by learning the sensory data statistics and distribution, the model is able to judiciously allocate resources for efficient representation and computation without any prior assumptions and simplifications. Alleviating the need for tedious design and parametrisation, it provides a flexible and robust approach to multisensory fusion, making it a promising candidate for robotic applications.
The core focus of my research interest is in developing sensor fusion mechanisms for robotic applications. These mechanisms enable a robot to obtain a consistent and global percept of its environment using available sensors by learning correlations between them in a distributed processing scheme inspired by cortical mechanisms.
Environmental interaction is a significant aspect in the life of every physical entity, which allows the updating of its internal state and acquiring new behaviors. Such interaction is performed by repeated iterations of a perception-cognition-action cycle, in which the entity acquires and memorizes relevant information from the noisily and partially observable environment, to develop a set of applicable behaviors (see Figure 5).
This recently started research project is in the area of mobile robotics, and more specifically in explicit methods applicable for acquiring and maintaining such environmental representations. State-of-the-art implementations build upon probabilistic reasoning algorithms, which typically aim at optimal solutions with the cost of high processing requirements.
In this project, we have developed an alternative, neurobiologically inspired method for real-time interpretation of sensory stimuli in mobile robotic systems: a distributed networked system with inter-merged information storage and processing that allows efficient parallel reasoning. This networked architecture will be comprised of interconnected heterogeneous software units, each encoding a different feature about the state of the environment that is represented by a local representation (see Figure 6).
Such extracted pieces of environmental knowledge interact by mutual influence to ensure overall system coherence. A sample instantiation of the developed system focuses on mobile robot heading estimation (see Figure 7). In order to obtain a robust and unambiguous description of robot’s current orientation within its environment inertial, proprioceptive and visual cues are fused (see image). Given available sensory data, the network relaxes to a globally consistent estimate of the robot’s heading angle and position.
Today’s trends in control engineering and robotics are blending gradually into a slightly challenging area, the development of fault-tolerant real-time applications. Hence, applications should timely deliver synchronized data-sets, minimize latency in their response and meet their performance specifications in the presence of disturbances. The fault-tolerant behavior in mobile robots refers to the possibility to autonomously detect and identify faults as well as the capability to continue operating after a fault occurred. This work introduces a real-time distributed control application with fault tolerance capabilities for differential wheeled mobile robots (see Figure 8).
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Furthermore, the application was extended to introduce a novel implementation for limited sensor mobile robots environment mapping. The developed algorithm is a SLAM implementation. It uses real-time data acquired from the sonar ring and uses this information to feed the mapping module for offline mapping (see Figure 9).
The latter is running on top of the real-time fault-tolerant control application for mobile robot trajectory tracking operation (see Figures 10, 11).
IRENA (Invariant Representations Extraction in Neural Architectures)
Team NeuroTHIx codebase. 1st Place at Merck Future of AI Research Challenge.
https://app.ekipa.de/challenge/future-of-ai/about
THI coverage:
https://www.thi.de/suche/news/news/thi-erfolgreich-in-ai-forschungswettbewerb
Merck Research Challenge aimed to generate insights from various disciplines that can lead to progress towards an understanding of invariant representation – that are novel and not based on Deep Learning.
IRENA (Invariant Representations Extraction in Neural Architectures) is the approach that team NeuroTHIx developed. IRENA offers a computational layer for extracting sensory relations for rich visual scenes, withy learning, inference, de-noising and sensor fusion capabilities. The system is also capable, through its underlying unsupervised learning capabilities, to embed semantics and perform scene understanding.
Using cortical maps as neural substrate for distributed representations of sensory streams, our system is able to learn its connectivity (i.e., structure) from the long-term evolution of sensory observations. This process mimics a typical development process where self-construction (connectivity learning), self-organization, and correlation extraction ensure a refined and stable representation and processing substrate. Following these principles, we propose a model based on Self-Organizing Maps (SOM) and Hebbian Learning (HL) as main ingredients for extracting underlying correlations in sensory data, the basis for subsequently extracting invariant representations.
Microsoft Faculty Connection coverage (http://goo.gl/uPWGna) for project demo at Hack Cambridge 2017, 28 – 29 January 2017, University of Cambridge with a Real-time Event-based Vision Monitoring and Notification System for Seniors and Elderly using Neural Networks.
It has been estimated that 33% of people age 65 will fall. At around 80, that increases to 50%. In case of a fall, seniors who receive help within an hour have a better rate of survival and, the faster help arrives, the less likely an injury will lead to hospitalization or the need to move into a long-term care facility. In such cases fast visual detection of abnormal motion patterns is crucial.
In this project we propose the use of a novel embedded Dynamic Vision Sensor (eDVS) for the task of classifying falls. Opposite from standard cameras which provide a time sequenced stream of frames, the eDVS provides only relative changes in a scene, given by individual events at the pixel level. Using this different encoding scheme the eDVS brings advantages over standard cameras. First, there is no redundancy in the data received from the sensor, only changes are reported. Second, as only events are considered the eDVS data rate is high. Third, the power consumption of the overall system is small, as just a low-end microcontroller is used to fetch events from the sensor and can ultimately run for long time periods in a battery powered setup. This project investigates how can we exploit the eDVS fast response time and low-redundancy in making decisions about elderly motion.
The computation back-end will be realized with a neural network classification to detect fall and filter outliers. The data will be provided from 2 stimuli (blinking LEDs at different frequencies) and will represent the actual position of the person wearing them. The changes in position of the stimuli will encode the possible positions corresponding to falls or normal cases.
We will use Microsoft Azure ML Studio to implement a MLP binary classifier for the 4 (2 stimuli x 2 Cartesian coordinates – (x,y) in the field of view) dimensional input. We labelled the data with Fall (F) and No Fall (NF).
Best innovation idea at the ANDRITZ Pioneers Hackaton innovating for the international technology group ANDRITZ. Developed an artificial neural learning agent for automation process productivity enhancement.
WIRED UK coverage ( http://goo.gl/5yQ1Fn ) at the Hack the Senses hackathon in London: How to hack your senses: from ‘seeing’ sound to ‘hair GPS’: “Two-man team HearSee built a headband that taps into synaesthesia, translating changes in frame-less video to sound allowing blind people or those with a weak vision to see motion. Roboticist and neurologist Cristian Axenie assembled the hardware in mere minutes – attaching a pair of cameras and wires to a terrycloth headband.”
Awarded 1st prize (team) at the Daimler Financial Services Big Data Analytics Hackaton for the design of a neuro-fuzzy learning system for anomaly detection and user interaction in big data streams.
Awarded special Microsoft Cognitive Technologies prize at the Burda Hackdays for the design of a neural learning system for inferring role assignments in working teams using psychometric data analytics.
Awarded 1st prize (team) in the BMW Automotive Hackdays for the design of an inference system for driving profile learning and recommendation for skills improvement and predictive maintenance in car-sharing.
