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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GF23-04080L" target="_blank" >GF23-04080L: Intuitivní spolupráce s domácím robotem během každodenních úloh</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

2025 IEEE International Conference on Development and Learning (ICDL)

ISBN

979-8-3315-4343-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE Conference Publications

Místo vydání

Piscataway

Místo konání akce

Praha

Datum konání akce

16. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—