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Related Experiment Video

Updated: Nov 19, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring.

Pedro Zuidberg Dos Martires¹, Nitesh Kumar¹, Andreas Persson²

¹Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This summary is machine-generated.

This study bridges symbolic and sub-symbolic AI by developing a semantic world model. It enables robots to learn from sensor data and reason about objects using probabilistic logic rules.

Keywords:

object tracking perceptual anchoring probabilistic anchoring probabilistic logic programming probabilistic rule learning relational particle filtering semantic world modeling statistical relational learning

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Robotic agents require integration of sub-symbolic sensor data processing with symbolic reasoning capabilities.
Bridging the gap between symbolic and sub-symbolic AI is crucial for advanced robot cognition.
Current approaches often struggle to reconcile continuous perceptual data with discrete symbolic representations.

Purpose of the Study:

To propose a novel semantic world modeling approach for robotic agents.
To enable robots to learn symbolic knowledge from sub-symbolic sensor data.
To facilitate reasoning about objects even when they are not directly observed.

Main Methods:

Object-centered semantic world modeling using bottom-up perceptual anchoring.
Extending anchoring to handle multi-modal probability distributions.
Coupling perceptual anchoring with probabilistic logic reasoning and statistical relational learning.

Main Results:

A framework combining perceptual anchoring and statistical relational learning was developed.
The system successfully maintains a semantic world model of perceived objects over time.
Demonstrated probabilistic reasoning over multi-modal distributions and learning of probabilistic logic rules.

Conclusions:

The proposed framework effectively integrates perception and reasoning for robotic agents.
Enables robots to learn symbolic world knowledge from noisy sensor input.
Validates the approach in scenarios with object interactions and occlusions requiring probabilistic inference.