PRAG: Procedural Action Sequence Symbolic Generator as a Mechanism for Autonomous Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00388335" target="_blank" >RIV/68407700:21730/25:00388335 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICDL63968.2025.11204420" target="_blank" >https://doi.org/10.1109/ICDL63968.2025.11204420</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDL63968.2025.11204420" target="_blank" >10.1109/ICDL63968.2025.11204420</a>

Result language

angličtina

Original language name

PRAG: Procedural Action Sequence Symbolic Generator as a Mechanism for Autonomous Learning

Original language description

Human development relies on a fundamental mechanism that enables the creation of novel tasks and their solutions, shaping cognitive and motor learning. This mechanism allows for the progressive refinement of problemsolving abilities. Unfortunately, such a mechanism is missing in recent robotic systems except for several works in the area of intrinsic motivation. Thus, it is desirable to develop a robotic ability to automatically generate diverse and solvable tasks and validate their feasibility in a continuous space. In this work, we introduce a novel system called PRAG, which serves as a generative mechanism for constructing multi-step manipulation tasks. This tool mirrors cognitive developmental processes by autonomously producing novel, structured challenges that can be progressively solved through interaction. PRAG requires just a set of known atomic actions, objects, and spatial predicates (semantic knowledge) as a starting point to output solvable task sequences of specified complexity. Validation occurs in two stages: high-level symbolic validation ensures logical and operational consistency, akin to how cognitive development refines action representations, while physical validation confirms task feasibility in a robotic environment, resembling embodied learning in human development. The generated tasks provide structured training data, facilitating progressive learning through curriculum-based approaches, much like the way children build on prior knowledge to master increasingly complex motor and cognitive skills. We tested PRAG on sequences with increasing complexity and demonstrated its capacity to produce millions of unique, solvable tasks. By drawing parallels between developmental mechanisms and task generation, we propose that our framework can contribute to understanding how structured learning environments shape problem-solving abilities in both artificial and biological systems.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GF23-04080L" target="_blank" >GF23-04080L: Intuitive Collaboration with Household Robots in Everyday Settings</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2025 IEEE International Conference on Development and Learning (ICDL)

ISBN

979-8-3315-4343-3

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

1-8

Publisher name

IEEE Conference Publications

Place of publication

Piscataway

Event location

Praha

Event date

Sep 16, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—