Building a Sensorimotor Representation of a Naive Agent’s Tactile Space

Marcel, Valentin; Argentieri, Sylvain; Gas, Bruno

doi:10.1109/tcds.2016.2617922

Cited by 11 publications

(23 citation statements)

References 16 publications

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“…It means that, at , two different motor states m 1 and m 2 can lead to the very same sensory input s = Ψ (m 1 ) = Ψ (m 2 ). As outlined in the previous authors work [19], one can then define an equivalence relation = Ψ , such that…”

Section: The Refinement Process : Partitioning Of the Motor Spacementioning

confidence: 99%

“…This dependency has been discussed in many publications [16], [17], [21], [22], and no clear solutions have been proposed so far to deal with this environment variability. In these works, the environment is systematically considered static, and they often restrict their study to cases where the environment changes do not influence the sensorimotor flow (by working on the agent body, for instance, like in [19] and [23]).…”

Section: The Refinement Process : Partitioning Of the Motor Spacementioning

confidence: 99%

“…The choice of Isomap is based on its simplicity and the fact that the neighborhood scale K is already available from the computation of the local similarity criterion K max in Eq. (19).…”

Section: B Evaluation Of the Representationmentioning

confidence: 99%

“…Indeed, as formalized later in this paper, the fact that the environmental state can possibly evolve along exploration has already proven to be a major theoretical difficulty [17], [21]. This paper proposes to tackle the environment dependency by generalizing the formalism initially proposed in [19]. Differently from this previous contribution, it is envisaged that the agent's end-effector is not sampling its own body anymore in such a way that all the sensory inputs are dependant to the environmental state.…”

Section: Introductionmentioning

confidence: 97%

“…Despite the use of a curvilinear component analysis (CCA) [18] and the definition of adapted Hausdorff distances in the agent motor space, this work lacks a proper mathematical formalization and they are no clear definitions on the properties or spaces that are actually captured by the agent. As a solution, the authors have demonstrated in [19] that topological properties of the space of sensory invariants on a sensitive body can also be well captured. This work focused on the agent self-interaction with its own body, whose perception is more deeply analyzed in [20] within the SMC theory framework.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Where Do I Move My Sensors? Emergence of a Topological Representation of Sensors Poses From the Sensorimotor Flow

Marcel

Argentieri

Gas

2021

IEEE Trans. Cogn. Dev. Syst.

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This paper deals with the perception of mobile robotic systems within the framework of interactive perception, and inspired by the sensorimotor contingencies (SMC) theory. These approaches state that perception arises from active exploration of an environment. In the SMC theory, it is postulated that information about the structure of space could be recovered from a quasi-uninterpreted sensorimotor flow. In a recent article, the authors have provided a mathematical framework for the construction of a sensorimotor representation of the interaction between the sensors and the body of a naive agent, provided that the sensory inputs come from the agent's own body. An extension of these results, with stimulations coming from an unknown changing environment, is proposed in this paper. More precisely it is demonstrated that, through repeated explorations of its motor configurations, the perceived sensory invariants can be exploited to build a topologically accurate internal representation of the relative poses of the agent's sensors in the physical world. Precise theoretical considerations are provided as well as an experimental framework. Finally, some examples that serve as proofs of concepts are analysed in both simulated and realistic environments.

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Section: The Refinement Process : Partitioning Of the Motor Spacementioning

confidence: 99%

Section: The Refinement Process : Partitioning Of the Motor Spacementioning

confidence: 99%

“…The choice of Isomap is based on its simplicity and the fact that the neighborhood scale K is already available from the computation of the local similarity criterion K max in Eq. (19).…”

Section: B Evaluation Of the Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Where Do I Move My Sensors? Emergence of a Topological Representation of Sensors Poses From the Sensorimotor Flow

Marcel

Argentieri

Gas

2021

IEEE Trans. Cogn. Dev. Syst.

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Body models in humans, animals, and robots: mechanisms and plasticity

Hoffmann

2021

Body Schema and Body Image

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Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth or failures, or using tools. The key foundation is an internal representation of the body that the agent—human, animal, or robot—has developed. In the biological realm, evidence has been accumulating in diverse disciplines, giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. This chapter compares the character of body representations in biology with their robotic counterparts and relates that to the differences in performance observed. Conclusions are drawn about how robots can inform the biological sciences dealing with body representations and which of the features of the ‘body in the brain’ should be transferred to robots, giving rise to more adaptive and resilient self-calibrating machines.

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A Sensorimotor Perspective on Grounding the Semantic of Simple Visual Features

Laflaquière

2018

2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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In Machine Learning and Robotics, the semantic content of visual features is usually provided to the system by a human who interprets its content. On the contrary, strictly unsupervised approaches have difficulties relating the statistics of sensory inputs to their semantic content without also relying on prior knowledge introduced in the system. We proposed in this paper to tackle this problem from a sensorimotor perspective. In line with the Sensorimotor Contingencies Theory, we make the fundamental assumption that the semantic content of sensory inputs at least partially stems from the way an agent can actively transform it. We illustrate our approach by formalizing how simple visual features can induce invariants in a naive agent's sensorimotor experience, and evaluate it on a simple simulated visual system. Without any a priori knowledge about the way its sensorimotor information is encoded, we show how an agent can characterize the uniformity and edge-ness of the visual features it interacts with.

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Building a Sensorimotor Representation of a Naive Agent’s Tactile Space

Cited by 11 publications

References 16 publications

Where Do I Move My Sensors? Emergence of a Topological Representation of Sensors Poses From the Sensorimotor Flow

Where Do I Move My Sensors? Emergence of a Topological Representation of Sensors Poses From the Sensorimotor Flow

Body models in humans, animals, and robots: mechanisms and plasticity

A Sensorimotor Perspective on Grounding the Semantic of Simple Visual Features

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