Online Event Segmentation in Active Perception using Adaptive Strong Anticipation
Bruno Nery, Rodrigo Ventura

TL;DR
This paper introduces an online event segmentation framework for active perception robots, using adaptive synchronization and anticipation to handle sensory latency and predict event boundaries in continuous perceptual streams.
Contribution
It presents a novel framework based on dynamical systems synchronization for real-time event segmentation in robots with active perception capabilities.
Findings
Successful implementation of the framework in a proof of concept
Preliminary results show effective detection of event boundaries
Framework accounts for sensory processing latency
Abstract
Most cognitive architectures rely on discrete representation, both in space (e.g., objects) and in time (e.g., events). However, a robot interaction with the world is inherently continuous, both in space and in time. The segmentation of the stream of perceptual inputs a robot receives into discrete and meaningful events poses as a challenge in bridging the gap between internal cognitive representations, and the external world. Event Segmentation Theory, recently proposed in the context of cognitive systems research, sustains that humans segment time into events based on matching perceptual input with predictions. In this work we propose a framework for online event segmentation, targeting robots endowed with active perception. Moreover, sensory processing systems have an intrinsic latency, resulting from many factors such as sampling rate, and computational processing, and which is…
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Taxonomy
TopicsNeural dynamics and brain function · Neural Networks and Applications · EEG and Brain-Computer Interfaces
