ArticlePloS one2016
Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Computational models of peripersonal space representation.Physics of life reviews · 2025Review
- Egocentric value maps of the near-body environment.Nature neuroscience · 2025Article
- A normative model of peripersonal space encoding as performing impact prediction.PLoS computational biology · 2022Article
- From statistical regularities in multisensory inputs to peripersonal space representation and body ownership: Insights from a neural network model.The European journal of neuroscience · 2021Article
- Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.Nature communications · 2020Article
- Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.Sensors (Basel, Switzerland) · 2019Article
- The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion.Cognitive processing · 2019Article
- Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Memory and mental time travel in humans and social robots.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Article
- Brain-Inspired Coding of Robot Body Schema Through Visuo-Motor Integration of Touched Events.Frontiers in neurorobotics · 2019Article
- Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping.Frontiers in neurorobotics · 2019Article
- How Cognitive Models of Human Body Experience Might Push Robotics.Frontiers in neurorobotics · 2019Article
- Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.Journal of neurophysiology · 2018Article
- Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics.Frontiers in neurorobotics · 2018Article
- iCub-HRI: A Software Framework for Complex Human-Robot Interaction Scenarios on the iCub Humanoid Robot.Frontiers in robotics and AI · 2018Article
- Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework.Cognitive computation · 2018Article
- Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin.PloS one · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.