ArticleFrontiers in neurorobotics2018
Know Your Body Through Intrinsic Goals.
Article in Frontiers in neurorobotics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Kick-starting concept formation with intrinsically motivated learning: the grounding by competence acquisition hypothesis.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- From Affordances to Abstract Words: The Flexibility of Sensorimotor Grounding.Brain sciences · 2021Review
- Prerequisites for an Artificial Self.Frontiers in neurorobotics · 2020Review
- Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping.Frontiers in neurorobotics · 2019Article
- Sensorimotor Contingencies as a Key Drive of Development: From Babies to Robots.Frontiers in neurorobotics · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The first "object" that newborn children play with is their own body. This activity allows them to autonomously form a sensorimotor map of their own body and a repertoire of actions supporting future cognitive and motor development. Here we propose the theoretical hypothesis, operationalized as a computational model, that this acquisition of body knowledge is not guided by random motor-babbling, but rather by autonomously generated goals formed on the basis of intrinsic motivations. Motor exploration leads the agent to discover and form representations of the possible sensory events it can cause with its own actions. When the agent realizes the possibility of improving the competence to re-activate those representations, it is intrinsically motivated to select and pursue them as goals. The model is based on four components: (1) a self-organizing neural network, modulated by competence-based intrinsic motivations, that acquires abstract representations of experienced sensory (touch) changes; (2) a selector that selects the goal to pursue, and the motor resources to train to pursue it, on the basis of competence improvement; (3) an echo-state neural network that controls and learns, through goal-accomplishment and competence, the agent's motor skills; (4) a predictor of the accomplishment of the selected goals generating the competence-based intrinsic motivation signals. The model is tested as the controller of a simulated simple planar robot composed of a torso and two kinematic 3-DoF 2D arms. The robot explores its body covered by touch sensors by moving its arms. The results, which might be used to guide future empirical experiments, show how the system converges to goals and motor skills allowing it to touch the different parts of own body and how the morphology of the body affects the formed goals. The convergence is strongly dependent on competence-based intrinsic motivations affecting not only skill learning and the selection of formed goals, but also the formation of the goal representations themselves.
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Registered trials
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