Gordon Cheng

Technical University of Munich, Institute for Cognitive Systems, Faculty Member

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Gordon Cheng holds the Chair for Cognitive Systems, he is Founder and Director of Institute for Cognitive Systems, Faculty of Electrical and Computer Engineering at Technical University of Munich, Munich/Germany. He is also the Head of the CoC for Neuro-Engineering - Center of Competence Neuro-Engineering in the Department of Electrical and Computer Engineering. Prof. Cheng is the Program Director of the Elite Master of Science program in Neuroengineering (MSNE) of the Elite Network of Bavaria.Formerly, he was the Head of the Department of Humanoid Robotics and Computational Neuroscience, ATR Computational Neuroscience Laboratories, Kyoto, Japan. He was the Group Leader for the JST International Cooperative Research Project (ICORP), Computational Brain. He has also been designated as a Project Leader/Research Expert for National Institute of Information and Communications Technology (NICT) of Japan. He is involved in a large number of major European Union Projects (e.g. RoboCub, Factory-in-a-Day, CONTEST-ITN, RoboCom-Flagship).Gordon Cheng is the co-inventor of approximately 20 patents and author of approximately 300 technical publications, proceedings, editorials and book chapters. He has been named IEEE Fellow 2017 for “contributions in humanoid robotic systems and neurorobotics”.

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Joao Bimbo

Istituto Italiano di Tecnologia / Italian Institute of Technology

Waseda University

Izzeldeen M O H A M M A D Badarin

Mohsen Kaboli

Technical University of Munich

Giorgio Cannata

Università degli Studi di Genova

David Vernon

University of Skövde

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Papers by Gordon Cheng

A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications

Proceedings of the IEEE, 2019

This article presents a holistic approach to engineer the artificial skin for robots with an exam... more

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Humanoid robotics and neuroscience: science, engineering and society (hardback)(series: frontiers in neuroengineering series)

Transferring skills to humanoid robots by extracting semantic representations from observations of human activities

Artificial Intelligence, 2015

In this study, we present a framework that infers human activities from observations using semant... more In this study, we present a framework that infers human activities from observations using semantic representations. The proposed framework can be utilized to address the difficult and challenging problem of transferring tasks and skills to humanoid robots. We propose a method that allows robots to obtain and determine a higher-level understanding of a demonstrator’s behavior via semantic representations. This abstraction from observations captures the “essence” of the activity, thereby indicating which aspect of the demonstrator’s actions should be executed in order to accomplish the required activity. Thus, a meaningful semantic description is obtained in terms of human motions and object properties. In addition, we validated the semantic rules obtained in different conditions, i.e., three different and complex kitchen activities: 1) making a pancake; 2) making a sandwich; and 3) setting the table. We present quantitative and qualitative results, which demonstrate that without any further training, our system can deal with time restrictions, different execution styles of the same task by several participants, and different labeling strategies. This means, the rules obtained from one scenario are still valid even for new situations, which demonstrates that the inferred representations do not depend on the task performed. The results show that our system correctly recognized human behaviors in real-time in around 87.44% of cases, which was even better than a random participant recognizing the behaviors of another human (about 76.68%). In particular, the semantic rules acquired can be used to effectively improve the dynamic growth of the ontology-base knowledge representation. Hence, this method can be used flexibly across different demonstrations and constraints to infer and achieve a similar goal to that observed. Furthermore, the inference capability introduced in this study was integrated into a joint space control loop for a humanoid robot, an iCub, for achieving similar goals to the human demonstrator online.

Effects of short-term piano training on measures of finger tapping, somatosensory perception and motor-related brain activity in patients with cerebral palsy

Neuropsychiatric disease and treatment, 2017

Playing a musical instrument demands the integration of sensory and perceptual information with m... more Playing a musical instrument demands the integration of sensory and perceptual information with motor processes in order to produce a harmonic musical piece. The diversity of brain mechanisms involved and the joyful character of playing an instrument make musical instrument training a potential vehicle for neurorehabilitation of motor skills in patients with cerebral palsy (CP). This clinical condition is characterized by motor impairments that can affect, among others, manual function, and limit severely the execution of basic daily activities. In this study, adolescents and adult patients with CP, as well as a group of typically developing children learned to play piano for 4 consecutive weeks, having completed a total of 8 hours of training. For ten of the participants, learning was supported by a special technical system aimed at helping people with sensorimotor deficits to better discriminate fingers and orient themselves along the piano keyboard. Potential effects of piano tra...

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2013 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 24

IEEE Transactions on Neural Networks and Learning Systems

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Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Scientific Reports, 2016

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in s... more Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

Embodied artificial agents for understanding human social cognition

Philosophical Transactions of the Royal Society B: Biological Sciences, 2016

In this paper, we propose that experimental protocols involving artificial agents, in particular ... more In this paper, we propose that experimental protocols involving artificial agents, in particular the embodied humanoid robots, provide insightful information regarding social cognitive mechanisms in the human brain. Using artificial agents allows for manipulation and control of various parameters of behaviour, appearance and expressiveness in one of the interaction partners (the artificial agent), and for examining effect of these parameters on the other interaction partner (the human). At the same time, using artificial agents means introducing the presence of artificial, yet human-like, systems into the human social sphere. This allows for testing in a controlled, but ecologically valid, manner human fundamental mechanisms of social cognition both at the behavioural and at the neural level. This paper will review existing literature that reports studies in which artificial embodied agents have been used to study social cognition and will address the question of whether various mechanisms of social cognition (ranging from lower- to higher-order cognitive processes) are evoked by artificial agents to the same extent as by natural agents, humans in particular. Increasing the understanding of how behavioural and neural mechanisms of social cognition respond to artificial anthropomorphic agents provides empirical answers to the conundrum &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;What is a social agent?&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;

運動学習プリミティブを用いたロボットの見まね学習(PRMU&NCテーマセッション(3) : 模倣学習)(認識と学習,模倣学習)

電子情報通信学会技術研究報告 Prmu パターン認識メディア理解, Oct 17, 2003

Representation and Control of the Task Space in Humans and Humanoid Robots

ABSTRACT

Complex continuous meaningful humanoid: A Multi Sensory-Cue Based Approach

Program Co-Chairs

運動学習プリミティブを用いた 2 足歩行ロボットの学習および適応

Humanoid Robotics and Neuroscience: Science, Engineering and Society

Balance Control

GORDON CHENG and ALEXANDER ZELINSKY

ABSTRACT

Examining human walking characteristics with a telescopic compass-like biped walker model

2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2004

ABSTRACT A model for biped walking is proposed. The model has a compass-like mechanism with teles... more ABSTRACT A model for biped walking is proposed. The model has a compass-like mechanism with telescopic type legs, and uses a simple leg stretch-contraction motion pattern. The model with a point shaped foot is able to represent human walking characteristics. A systematic conversion from the point shaped foot to the equivalent of a normal surface foot - resembling the shape that of humans, this extension preserves the same human-like walking characteristics while gaining the benefits of added stability and the reduction of impact forces during foot contact.

Real-Time Vision Based Mouth Tracking and Parameterization for a Humanoid Imitation Task

ABSTRACT

Toward Cognitive Humanoid Robots

International Journal of Humanoid Robotics

Constraint-based Ground contact handling in Humanoid Robotics Simulation

This paper presents a method for resolving contact in dynamic simulations of articulated figures.... more This paper presents a method for resolving contact in dynamic simulations of articulated figures. It is intended for humanoids with polygonal feet and incorporates Coulomb friction exactly. The proposed technique is based on a constraint selection paradigm. Its implementation offers an exact mode which guarantees correct behavior, as well as an efficiency optimized mode which sacrifices accuracy for a tightly bounded computational burden, thus facilitating batch simulations or the use of highly detailed foot models. The method has been applied to the simulation of two humanoid robots, CB and Hoap-2, and validated with respect to human and robot motions recorded in reality and in simulation. The friction parameters of contact have also been established empirically.

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Uniform Cellular Design of Artificial Robotic Skin

ABSTRACT

A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications

Proceedings of the IEEE, 2019

This article presents a holistic approach to engineer the artificial skin for robots with an exam... more

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Humanoid robotics and neuroscience: science, engineering and society (hardback)(series: frontiers in neuroengineering series)

Transferring skills to humanoid robots by extracting semantic representations from observations of human activities

Artificial Intelligence, 2015