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Sensorimotor Proto-Objects: Bootstrapping Perception from Compensable Actions
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AI
DOI：10.1109/tcds.2026.3731855.png)
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Abstract
En 中文
Sensorimotor contingencies theory provides a strong foundation for a mathematical formalization of the bootstrapping problem of robot perception. Currently, this framework lacks the ability to deal with the dynamics of the environment and, by extension, objects. This paper shows that a sensorimotor agent with prediction functions acquired by motor babbling can infer environment dynamics and detect primitive object notions. To do that, we formalize Poincaré’s notion of compensation in terms of the agent’s action group, establishing an isomorphism with the environmental displacements. This allows the agent to describe environmental spatial transformations in terms of actions. We then conduct two experiments applying those developments to a visual task to (1) infer environmental global displacements and (2) infer a single object’s segmentation via its movement in the environment. We define a notion of sensorimotor proto-objects, as sensory elements whose information moves rigidly and coherently while maintaining shape and size. Here, the agent identifies the displacement by inferring the one that minimizes prediction error which is computed thanks to an internally built sensory metric. Results show that, in a simple 2D visual environment, the agent robustly infers the displacement observed for both the global and local case, but also accurately segments a single proto-object. By formalizing and inferring environmental transformation, this work introduces a new definition of sensorimotor proto-objects paving the way for a new understanding of how object perception can emerge from an agent’s own actions.
Keywords:
sensorimotor contingencies
bootstrapping
embodied cognition
perceptual organization
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