Online Event Segmentation in Active Perception using Adaptive Strong Anticipation

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 seldom accounted for. This framework is founded on the theory of dynamical systems synchronization, where the system considered includes both the robot and the world coupled (strong anticipation). An adaption rule is used to perform simultaneous system identification and synchronization, and anticipating synchronization is employed to predict the short-term system evolution. This prediction allows for an appropriate control of the robot actuation. Event boundaries are detected once synchronization is lost (sudden increase of the prediction error). An experimental proof of concept of the proposed framework is presented, together with some preliminary results corroborating the approach.