Artificial nociception and motor responses to pain, for humans and robots

Bagnato, Carlo; Takagi, Atsushi; Burdet, Etienne

doi:10.1109/embc.2015.7320102

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Specifically, the tendency by subjects to reduce elbow collisions at the cost of hand collisions reveals our inability to decouple the motor task, and underlies hierarchical processes. Such results further reveal how humans prevent damage, pain and injury 2,17 , adding to our fundamental understanding of motor control. Possibly, they will give insight into the implementation of artificial pain mechanisms for robots 26 , serving as reference to inspire the design of safe robotic 27 and prosthetic 28 retraction movements.…”

Section: Discussionmentioning

confidence: 76%

Arm movement adaptation to concurrent pain constraints

Kühn

Bagnato

Burdet

et al. 2021

Sci Rep

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How do humans coordinate their movements in order to avoid pain? This paper investigates a motor task in the presence of concurrent potential pain sources: the arm must be withdrawn to avoid a slap on the hand while avoiding an elbow obstacle with an electrical noxious stimulation. The results show that our subjects learned to control the hand retraction movement in order to avoid the potential pain. Subject-specific motor strategies were used to modify the joint movement coordination to avoid hitting the obstacle with the elbow at the cost of increasing the risk of hand slap. Furthermore, they used a conservative strategy as if assuming an obstacle in 100% of the trials.

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Section: Discussionmentioning

confidence: 76%

Arm movement adaptation to concurrent pain constraints

Kühn

Bagnato

Burdet

et al. 2021

Sci Rep

Self Cite

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“…Pain is often excruciating and debilitating, but fundamentally it is a unique adaptive mechanism by which the organism modulates incoming sensory information to prevent injuries or damage from being exacerbated. [ 231,232 ] Implementing pain perception using electronic/iontronic devices could add nociceptive capabilities to artificial robots to prevent undue harm when interacting with the environment and help restore sensory function in patients with pain loss. [ 233–235 ] Nociceptors are key receptors that recognize pain (i.e., noxious stimuli above a certain threshold) and send warning signals to the central nervous system [ 236,237 ] ( Figure a).…”

Section: Low‐level Neuromorphic Sensory Computing Based On Iontronic ...mentioning

confidence: 99%

“…Pain is often excruciating and debilitating, but fundamentally it is a unique adaptive mechanism by which the organism modulates incoming sensory information to prevent injuries or damage from being exacerbated. [231,232] Implementing pain perception using electronic/iontronic devices could add nociceptive capabilities to artificial robots to prevent undue harm when [108] Copyright 2022, The authors. c) Schematic illustration of the 5-by-5 array of the In 2 O 3 optoelectronic synaptic transistor.…”

Section: Pain-perception Neuromorphic Sensorsmentioning

confidence: 99%

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Dai

Liu

et al. 2023

Advanced Materials

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Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic‐electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron‐based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low‐level and high‐level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

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“…We believe that the definition of Robot Pain should be inspired by the nature of organisms' pain. First, the pain mechanism in organisms has evolved over thousands of years, and it is closely related to physical injury (Bagnato et al, 2015 ). Broom proposed that pain is the neural activity at the brain level that accompanies physical injury (Broom, 2001 ).…”

Section: Introductionmentioning

confidence: 99%

A brain-inspired robot pain model based on a spiking neural network

Feng

Zeng

2022

Front. Neurorobot.

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IntroductionPain is a crucial function for organisms. Building a “Robot Pain” model inspired by organisms' pain could help the robot learn self-preservation and extend longevity. Most previous studies about robots and pain focus on robots interacting with people by recognizing their pain expressions or scenes, or avoiding obstacles by recognizing dangerous objects. Robots do not have human-like pain capacity and cannot adaptively respond to danger. Inspired by the evolutionary mechanisms of pain emergence and the Free Energy Principle (FEP) in the brain, we summarize the neural mechanisms of pain and construct a Brain-inspired Robot Pain Spiking Neural Network (BRP-SNN) with spike-time-dependent-plasticity (STDP) learning rule and population coding method.MethodsThe proposed model can quantify machine injury by detecting the coupling relationship between multi-modality sensory information and generating “robot pain” as an internal state.ResultsWe provide a comparative analysis with the results of neuroscience experiments, showing that our model has biological interpretability. We also successfully tested our model on two tasks with real robots—the alerting actual injury task and the preventing potential injury task.DiscussionOur work has two major contributions: (1) It has positive implications for the integration of pain concepts into robotics in the intelligent robotics field. (2) Our summary of pain's neural mechanisms and the implemented computational simulations provide a new perspective to explore the nature of pain, which has significant value for future pain research in the cognitive neuroscience field.

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Artificial nociception and motor responses to pain, for humans and robots

Cited by 7 publications

References 17 publications

Arm movement adaptation to concurrent pain constraints

Arm movement adaptation to concurrent pain constraints

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

A brain-inspired robot pain model based on a spiking neural network

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