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Tytuł: Contact models in robotic grasp learning

Autor:

Kopicki, Marek

Data wydania:

2026

Typ zasobu:

artykuł

Współtwórca:

Okarma, Krzysztof - ed. ; Skrzypczyński, Piotr - ed. ; Stefanowski, Jerzy - ed.

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 36 (2026)

Abstract:

Autonomous robotic manipulation faces persistent challenges in dynamic, unstructured environments, where traditional physics-based models struggle with incomplete or inaccurate object and contact data. Machine learning has enabled adaptive manipulation skills, but purely end-to-end approaches often make contact-level reasoning implicit and rely on extensive training data. ; Structured methods that incorporate segmentation, depth, and planning offer complementary, more interpretable alternatives. Contact modelling plays a central role in predicting object motion and enabling realistic simulations, especially at close-range interactions. Early forward models demonstrated generalization to unseen objects, but struggled with action generation and exemplar dependence. ; Later advances introduced generative contact models that infer grasps directly from depth images, enabling one-shot learning and robust manipulation without prior object knowledge. This paper reviews and compares such contact modelling approaches, highlighting their role in grasp synthesis, generalization, and integration into dynamic and perception-driven manipulation pipelines.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Identyfikator zasobu:

oai:zbc.uz.zgora.pl:98922

DOI:

10.61822/amcs-2026-0029

Strony:

445-458

Źródło:

AMCS, volume 36, number 3 (2026)

Jezyk:

eng

Licencja CC BY 4.0:

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Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego

Nazwa wydania Data
Contact models in robotic grasp learning 6 lis 2026

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