Object

Title: Contact models in robotic grasp learning

Creator:

Kopicki, Marek

Date:

2026

Resource Type:

artykuł

Contributor:

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

Subtitle:

.

Group publication title:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:98922

DOI:

10.61822/amcs-2026-0029

Pages:

445-458

Source:

AMCS, volume 36, number 3 (2026)

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Edition name Date
Contact models in robotic grasp learning Nov 6, 2026

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