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Khan MS, Olds JL. When neuro-robots go wrong: A review. Front Neurorobot 2023; 17:1112839. [PMID: 36819005 PMCID: PMC9935594 DOI: 10.3389/fnbot.2023.1112839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.
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Krauhausen I, Koutsouras DA, Melianas A, Keene ST, Lieberth K, Ledanseur H, Sheelamanthula R, Giovannitti A, Torricelli F, Mcculloch I, Blom PWM, Salleo A, van de Burgt Y, Gkoupidenis P. Organic neuromorphic electronics for sensorimotor integration and learning in robotics. SCIENCE ADVANCES 2021; 7:eabl5068. [PMID: 34890232 PMCID: PMC8664264 DOI: 10.1126/sciadv.abl5068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.
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Affiliation(s)
- Imke Krauhausen
- Max Planck Institute for Polymer Research, Mainz, Germany
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Armantas Melianas
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Exponent, 149 Commonwealth Dr, Menlo Park, CA 94025, USA
| | - Scott T. Keene
- Department of Engineering, University of Cambridge, Cambridge, UK
| | | | | | - Rajendar Sheelamanthula
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Alexander Giovannitti
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
| | - Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
| | - Iain Mcculloch
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Department of Chemistry, University of Oxford, Oxford, UK
| | | | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Yoeri van de Burgt
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Paschalis Gkoupidenis
- Max Planck Institute for Polymer Research, Mainz, Germany
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
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Chen G, Bing Z, Rohrbein F, Conradt J, Huang K, Cheng L, Jiang Z, Knoll A. Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2017.2712712] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Krichmar JL. Neurorobotics-A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots. Front Neurorobot 2018; 12:42. [PMID: 30061820 PMCID: PMC6054919 DOI: 10.3389/fnbot.2018.00042] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 06/25/2018] [Indexed: 01/30/2023] Open
Abstract
Neurorobots are robots whose control has been modeled after some aspect of the brain. Since the brain is so closely coupled to the body and situated in the environment, Neurorobots can be a powerful tool for studying neural function in a holistic fashion. It may also be a means to develop autonomous systems that have some level of biological intelligence. The present article provides my perspective on this field, points out some of the landmark events, and discusses its future potential.
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Affiliation(s)
- Jeffrey L. Krichmar
- Department of Cognitive Sciences, University of California Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
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Iqbal M, Rehan M, Hong KS. Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh-Nagumo Neurons under Direction-Dependent Coupling. Front Neurorobot 2018. [PMID: 29535622 PMCID: PMC5834533 DOI: 10.3389/fnbot.2018.00006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided.
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Affiliation(s)
- Muhammad Iqbal
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
| | - Muhammad Rehan
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
| | - Keum-Shik Hong
- Department of Cogno-Mechatronics Engineering, School of Mechanical Engineering, Pusan National University, Busan, South Korea
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Milde MB, Blum H, Dietmüller A, Sumislawska D, Conradt J, Indiveri G, Sandamirskaya Y. Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System. Front Neurorobot 2017; 11:28. [PMID: 28747883 PMCID: PMC5507184 DOI: 10.3389/fnbot.2017.00028] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.
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Affiliation(s)
- Moritz B Milde
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Hermann Blum
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Alexander Dietmüller
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Dora Sumislawska
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Jörg Conradt
- Neuroscientific System Theory, Department of Electrical and Computer Engineering, Technical University of MunichMunich, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
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Vannucci L, Falotico E, Laschi C. Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model. Front Neurosci 2017; 11:341. [PMID: 28659756 PMCID: PMC5469895 DOI: 10.3389/fnins.2017.00341] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 05/30/2017] [Indexed: 11/13/2022] Open
Abstract
Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders) is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.
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Affiliation(s)
- Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore Sant'AnnaPontedera, Italy
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant'AnnaPontedera, Italy
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant'AnnaPontedera, Italy
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van der Velde F. Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing. Front Neurorobot 2016; 10:4. [PMID: 27242504 PMCID: PMC4869607 DOI: 10.3389/fnbot.2016.00004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/02/2016] [Indexed: 11/15/2022] Open
Abstract
In situ concept-based computing is based on the notion that conceptual representations in the human brain are “in situ.” In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired “blackboards.” The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.
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Affiliation(s)
- Frank van der Velde
- Technical Cognition, CPE-BMS and CTIT, University of Twente , Enschede , Netherlands
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