A hierarchical sensorimotor control framework for human-in-the-loop robotic hands

Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands. Description Principles of hierarchical human sensorimotor control promise improved human-in-the-loop control of sensate robotic hands.

[1]  G. Loeb Developing Intelligent Robots that Grasp Affordance , 2022, Frontiers in Robotics and AI.

[2]  Yulia Sandamirskaya,et al.  Neuro-inspired electronic skin for robots , 2022, Science Robotics.

[3]  S. Došen,et al.  Full-hand electrotactile feedback using electronic skin and matrix electrodes for high-bandwidth human–machine interfacing , 2022, Philosophical Transactions of the Royal Society A.

[4]  Georg Martius,et al.  A soft thumb-sized vision-based sensor with accurate all-round force perception , 2021, Nat. Mach. Intell..

[5]  Giuseppe Averta Human-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices , 2022, Springer Tracts in Advanced Robotics.

[6]  A. Bicchi,et al.  Comparison between rigid and soft poly-articulated prosthetic hands in non-expert myo-electric users shows advantages of soft robotics , 2021, Scientific Reports.

[7]  T. Nanayakkara,et al.  Adapting the visuo-haptic perception through muscle coactivation , 2021, Scientific Reports.

[8]  G. C. Bettelani,et al.  The Interaction between Motion and Texture in the Sense of Touch. , 2021, Journal of neurophysiology.

[9]  S. Micera,et al.  Toward higher-performance bionic limbs for wider clinical use , 2021, Nature Biomedical Engineering.

[10]  Nathan F. Lepora,et al.  Soft Biomimetic Optical Tactile Sensing With the TacTip: A Review , 2021, IEEE Sensors Journal.

[11]  Silvestro Micera,et al.  Restoration of sensory information via bionic hands , 2020, Nature Biomedical Engineering.

[12]  Jonathon W. Sensinger,et al.  A Review of Sensory Feedback in Upper-Limb Prostheses From the Perspective of Human Motor Control , 2020, Frontiers in Neuroscience.

[13]  Loredana Zollo,et al.  Methods and Sensors for Slip Detection in Robotics: A Survey , 2020, IEEE Access.

[14]  Haiwen Luan,et al.  Skin-integrated wireless haptic interfaces for virtual and augmented reality , 2019, Nature.

[15]  Nicolas Sommer,et al.  Shared human–robot proportional control of a dexterous myoelectric prosthesis , 2019, Nature Machine Intelligence.

[16]  Michael M. Halassa,et al.  Thalamocortical Circuit Motifs: A General Framework , 2019, Neuron.

[17]  Fulvio Mastrogiovanni,et al.  Active Haptic Perception in Robots: A Review , 2019, Front. Neurorobot..

[18]  Kazuhiko Seki,et al.  Gain control in the sensorimotor system. , 2019, Current opinion in physiology.

[19]  Claudio Castellini,et al.  Robotic interfaces for cognitive psychology and embodiment research: A research roadmap. , 2018, Wiley interdisciplinary reviews. Cognitive science.

[20]  David A. Abbink,et al.  Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics , 2018, Front. Neurorobot..

[21]  Gerald E Loeb,et al.  Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies , 2018, Applied bionics and biomechanics.

[22]  Craig S. Chapman,et al.  Decision-making in sensorimotor control , 2018, Nature Reviews Neuroscience.

[23]  Nitish V. Thakor,et al.  Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain , 2018, Science Robotics.

[24]  Matthew T. Kaufman,et al.  Perspectives on classical controversies about the motor cortex. , 2017, Journal of neurophysiology.

[25]  Christian Cipriani,et al.  Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Matteo Bianchi,et al.  Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands". , 2016, Physics of life reviews.

[27]  Matteo Bianchi,et al.  Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands. , 2016, Physics of life reviews.

[28]  Greg Morgan,et al.  The Onlife Manifesto: Being Human in a Hyperconnected Era , 2015 .

[29]  Maurizio Valle,et al.  Tactile data processing method for the reconstruction of contact force distributions , 2015 .

[30]  L. Floridi The Onlife Manifesto: Being Human in a Hyperconnected Era , 2014 .

[31]  Gerald E. Loeb,et al.  Multimodal Tactile Sensor , 2014, The Human Hand as an Inspiration for Robot Hand Development.

[32]  V. Hayward,et al.  Finger pad friction and its role in grip and touch , 2013, Journal of The Royal Society Interface.

[33]  David Vernon,et al.  Research road map , 2010 .

[34]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

[35]  D. Wolpert,et al.  Abnormalities in the awareness of action , 2002, Trends in Cognitive Sciences.

[36]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[37]  H. Baxter Williams,et al.  A Survey , 1992 .

[38]  Zengo Furukawa,et al.  A General Framework for , 1991 .

[39]  R. Klatzky,et al.  Hand movements: A window into haptic object recognition , 1987, Cognitive Psychology.

[40]  Robert C. Wolpert,et al.  A Review of the , 1985 .