Semantic State Estimation in Robot Cloth Manipulations Using Domain Adaptation from Human Demonstrations
Deformable object manipulations, such as those involving textiles, present a significant challenge due to their high dimensionality and complexity. In this paper, we propose a solution for estimating semantic states in cloth manipulation tasks. To this end, we introduce a new, large-scale, fully-ann...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/388092 |
| Acesso em linha: | http://hdl.handle.net/10261/388092 https://api.elsevier.com/content/abstract/scopus_id/85190696583 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Cloth Domain Adaptation Garment Manipulation Robotic Perception Semantics Transfer Learning |
| Resumo: | Deformable object manipulations, such as those involving textiles, present a significant challenge due to their high dimensionality and complexity. In this paper, we propose a solution for estimating semantic states in cloth manipulation tasks. To this end, we introduce a new, large-scale, fully-annotated RGB image dataset of semantic states featuring a diverse range of human demonstrations of various complex cloth manipulations. This effectively transforms the problem of action recognition into a classification task. We then evaluate the generalizability of our approach by employing domain adaptation techniques to transfer knowledge from human demonstrations to two distinct robotic platforms: Kinova and UR robots. Additionally, we further improve performance by utilizing a semantic state graph learned from human manipulation data. |
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