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Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework [article]

Yashraj S. Narang, Karl Van Wyk, Arsalan Mousavian, Dieter Fox
2020 arXiv   pre-print
These data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible.  ...  element (FE) model of the BioTac, which complements the experimental dataset with high-resolution, distributed contact data, and 3) neural-network-based mappings from raw BioTac signals to low-dimensional  ...  preliminary mechanical data on the rubber skin; and Balakumar Sundaralingam for providing data from his previous analyses.  ... 
arXiv:2006.03777v1 fatcat:2isf44sgffdz7aj664ry3gvn3i

Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections [article]

Yashraj Narang, Balakumar Sundaralingam, Miles Macklin, Arsalan Mousavian, Dieter Fox
2021 arXiv   pre-print
Tactile sensing is critical for robotic grasping and manipulation of objects under visual occlusion.  ...  However, in contrast to simulations of robot arms and cameras, current simulations of tactile sensors have limited accuracy, speed, and utility.  ...  Guo, and A. Bakshi for their collaboration with Isaac Gym, as well as K. Van Wyk, A. Handa, and T. Hermans for their feedback.  ... 
arXiv:2103.16747v1 fatcat:pp4n544m2fdqhj33buyik5cs44

Action Conditioned Tactile Prediction: a case study on slip prediction [article]

Willow Mandil, Kiyanoush Nazari, Amir Ghalamzan E
2022 arXiv   pre-print
In this paper, we presented two novel data-driven action-conditioned models for predicting tactile signals during real-world physical robot interaction tasks (1) action condition tactile prediction and  ...  We use a magnetic-based tactile sensor that is challenging to analyse and test state-of-the-art predictive models and the only existing bespoke tactile prediction model.  ...  DISCUSSION AND CONCLUSION We presented two novel data-driven predictive models for tactile signals during real-world physical robot interaction tasks.  ... 
arXiv:2205.09430v1 fatcat:bwytnn3a7rdgnii7j67c7lxnrm

6G Enabled Tactile Internet and Cognitive Internet of Healthcare Everything: Towards a Theoretical Framework

Prafulla Kumar Padhi, Feranando Charrua-Santos
2021 Applied System Innovation  
Based on the 6GCIoHE system architecture, applications, and challenges, the aim of this study was accomplished by developing a novel theoretical framework to captivate further research within the cognitive  ...  To remotely detect patients' emotional state of diagnosing diseases, the integration of 6G enabled Tactile Internet, cognitive data intelligence, and Internet of Healthcare Everything is proposed to form  ...  Sensors Medical sensors are the eyes and the ears of the IoHE network. Sensors convert physical signals into electrical signals.  ... 
doi:10.3390/asi4030066 fatcat:sq72jk2n3neffhw5zhu54tteyu

A Sense of Touch for the Shadow Modular Grasper

Nicholas Pestell, Luke Cramphorn, Fotios Papadopoulos, Nathan Lepora
2019 IEEE Robotics and Automation Letters  
This constraint is not limited to the presented hardware or method since it is produced by a physical singularity observed in the raw tactile signal.  ...  data-driven approaches.  ... 
doi:10.1109/lra.2019.2902434 fatcat:ikdcxdy3r5frfp2duezbftt56q

A survey of Tactile Human–Robot Interactions

Brenna D. Argall, Aude G. Billard
2010 Robotics and Autonomous Systems  
This article presents a review of current research within the field of Tactile Human-Robot Interactions (Tactile HRI), where physical contact from a human is detected by a robot during the execution or  ...  Robots come into physical contact with humans in both experimental and operational settings.  ...  Acknowledgements The research leading to these results has received funding from the European Community's Seventh Framework Programme  ... 
doi:10.1016/j.robot.2010.07.002 fatcat:b7wxsau5a5a37j3yu6orlotnuy

TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors [article]

Jingxi Xu, Shuran Song, Matei Ciocarlie
2022 arXiv   pre-print
We demonstrate this method on a tactile object recognition task, where a robot equipped with a touch sensor must explore and identify an object from a known set based on binary contact signals alone.  ...  However, tactile sensing presents the challenge of being an active sensing modality: a touch sensor provides sparse, local data, and must be used in conjunction with effective exploration strategies in  ...  In our proposed architecture, both the explorer and the discriminator are learned using data-driven methods; in particular, the explorer is trained via reinforcement learning (RL) and the discriminator  ... 
arXiv:2203.00798v3 fatcat:u35xyginsbbzbfulez2aaga7uu

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †

Ali Ibrahim, Paolo Gastaldo, Hussein Chible, Maurizio Valle
2017 Sensors  
In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing.  ...  It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption.  ...  Author Contributions: Ali Ibrahim was involved in the design and implementation of the embedded digital signal processing systems for tactile data processing, and contributed in writing/editing the abstract  ... 
doi:10.3390/s17030558 pmid:28287448 pmcid:PMC5375844 fatcat:hwgffzuakbb3tklbmyifgcrbzi

Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period

Yasuo Kuniyoshi
2019 Philosophical Transactions of the Royal Society of London. Biological Sciences  
Driven by spinal nonlinear oscillator circuits coupled together via embodiment, somatosensory signals are evoked and learned by a model of the cerebral cortex with 2.6 million neurons and 5.3 billion synapses  ...  Then our model and experiments on emergent embodied behaviour are presented. They consist of chaotic maps embedded in sensory-motor loops and coupled via embodiment.  ...  Hoshinori Kanazawa helped preparation of some of the figures and online supplement to [53] .  ... 
doi:10.1098/rstb.2018.0031 pmid:30852992 pmcid:PMC6452254 fatcat:ebmqbqydf5gypntxakyy2yhgxu

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [article]

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
2021 arXiv   pre-print
However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion  ...  New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described.  ...  Here, we present operation research based theory-driven and machine learning based data-driven approaches for designing efficient resource allocation optimization algorithms. • A holistic end-to-end architecture  ... 
arXiv:2111.12444v1 fatcat:crrbtfylvjeihogumggdnxcbpq

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

Patrick Kasi, James Wright, Heba Khamis, Ingvars Birznieks, André van Schaik, Maurice J. Chacron
2016 PLoS ONE  
We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents.  ...  First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change.  ...  Setup and programmed robotic device for use in during data acquisition: HK PK IB.  ... 
doi:10.1371/journal.pone.0153366 pmid:27077750 pmcid:PMC4831826 fatcat:ncwyxjtvifdg5doykfrdwklifi

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

Adeeb Salh, Lukman Audah, Nor Shahida Mohd Shah, Abdulraqeb Alhammadi, Qazwan Abdullah, Yun Hee Kim, Samir A. Al-Gailani, Shipun A. Hamzah, Bashar A. F. Esmail, Akram A. Almohammedi
2021 IEEE Access  
In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based  ...  The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture.  ...  The importance aware scheduling decisions for efficient edge learning is based on multiple beams via multiple RF (MBMRF), achievement channel diversity and data diversity, and data sample signal is defined  ... 
doi:10.1109/access.2021.3069707 fatcat:v5om5kdtpze3vcxm6tko3efha4

Table of Contents

2022 IEEE Robotics and Automation Letters  
and Stiffness Analysis of a Novel 7-DOF Cable-Driven Manipulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..Cong KNODE-MPC: A Knowledge-Based Data-Driven Predictive  ...  Inaba A Data Driven Approach for Predicting Preferred Ankle Stiffness of a Quasi-Passive Prosthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lra.2022.3165102 fatcat:enjzebowe5hn7hsfwklc7nieuy

Modality-Specific Perceptual Expectations Selectively Modulate Baseline Activity in Auditory, Somatosensory, and Visual Cortices

Robert Langner, Thilo Kellermann, Frank Boers, Walter Sturm, Klaus Willmes, Simon B. Eickhoff
2011 Cerebral Cortex  
cortices and decreased activity in irrelevant ones.  ...  modulate signal-detection sensitivity by differentially modifying background activity (i.e., noise level) in different input channels.  ...  ., and S.B.E.); Human Brain Project (R01-MH074457-01A1 to S.B.E.); Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (Human Brain Model to S.B.E  ... 
doi:10.1093/cercor/bhr083 pmid:21527785 fatcat:72zb23shrjbw5jjshooxgcfkbm

2018-2020 Index Proceedings of the IEEE Vol. 106-108

2020 Proceedings of the IEEE  
, A., see Ciller, P., 1872-1905 Gonzalez-Garcia, T., see Gomez-Exposito, A., JPROC April 2018 626-660 Gonzalez-Lara, J., see Gomez-Exposito, A., JPROC April 2018 626-660 Good, N., see Martinez Cesena  ...  ., and Qi, J., Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction; JPROC Jan. 2020 51-68 Gonugondla, S.K., see Kang, M., 2251-2275 Gonzalez-De-Aledo, P., see -2039 Gonzalez-Garcia  ...  ., +, JPROC Nov. 2018 1879-1901 Model-Based and Data-Driven Strategies in Medical Image Computing. Optimization for Data-Driven Learning and Control.  ... 
doi:10.1109/jproc.2020.3040096 fatcat:35vqtzlkgjhzdhds5dbqyccesy
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