| 1. |
- Longhini, Alberta, et al.
(författare)
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EDO-Net : Learning Elastic Properties of Deformable Objects from Graph Dynamics
- 2023
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Ingår i: Proceedings - ICRA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3875-3881
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Konferensbidrag (refereegranskat)abstract
- We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of elastic physical properties of cloth-like deformable objects that can be extracted, for example, from a pulling interaction. In this paper we propose EDO-Net (Elastic Deformable Object - Net), a model of graph dynamics trained on a large variety of samples with different elastic properties that does not rely on ground-truth labels of the properties. EDO-Net jointly learns an adaptation module, and a forward-dynamics module. The former is responsible for extracting a latent representation of the physical properties of the object, while the latter leverages the latent representation to predict future states of cloth-like objects represented as graphs. We evaluate EDO-Net both in simulation and real world, assessing its capabilities of: 1) generalizing to unknown physical properties, 2) transferring the learned representation to new downstream tasks.
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| 2. |
- Longhini, Alberta, et al.
(författare)
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Elastic Context : Encoding Elasticity for Data-driven Models of Textiles
- 2023
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Ingår i: Proceedings - ICRA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1764-1770
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Konferensbidrag (refereegranskat)abstract
- Physical interaction with textiles, such as assistivedressing or household tasks, requires advanced dexterous skills.The complexity of textile behavior during stretching and pullingis influenced by the material properties of the yarn and bythe textile’s construction technique, which are often unknownin real-world settings. Moreover, identification of physicalproperties of textiles through sensing commonly available onrobotic platforms remains an open problem. To address this,we introduce Elastic Context (EC), a method to encode theelasticity of textiles using stress-strain curves adapted fromtextile engineering for robotic applications. We employ EC tolearn generalized elastic behaviors of textiles and examine theeffect of EC dimension on accurate force modeling of real-worldnon-linear elastic behaviors.
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| 3. |
- Marchetti, Giovanni Luca, et al.
(författare)
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Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation
- 2021
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Ingår i: 2021 20Th International Conference On Advanced Robotics (ICAR). - : IEEE. ; , s. 955-960
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Konferensbidrag (refereegranskat)abstract
- Manipulation of deformable objects has long been a challenging task in robotics. Their high-dimensional configuration space and complex dynamics make them difficult to consider for tasks such as robotic manipulation. In this paper, we address the problem of learning efficient representations of deformable objects which lend themselves better suitable for downstream robotics tasks. In particular, we consider graph-based representations of deformable objects which arise naturally from their point-cloud representation. Through manipulation, we learn to coarsen this graph into a simpler representation which still captures the necessary dynamics of the object. Our model consists of (a) a Cluster Assignment Model which takes the initial graph and coarsens it, (b) a Coarsened Dynamics Model that approximates the dynamics of the coarsened graph and (c) a Forward Prediction Model which predicts the next state of the ground truth graph. After end-to-end training, the Cluster Assignment Model learns to build coarse representations which better capture the dynamics compared to conventional clustering methods such as K-means. We evaluate our method on three sets of experiments: rigid objects, rigid objects with pair-wise interactions and a simulated dataset of a shirt.
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| 4. |
- Moletta, Marco, et al.
(författare)
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A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding
- 2023
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Ingår i: 2023 IEEE-RAS 22nd International Conference on Humanoid Robots. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the folding plans for different classes of garments, allowing for replicating the folding on unseen items of the same class. We evaluate the framework in the context of automating garment folding tasks. A quantitative analysis is performed on three classes of garments, demonstrating that the framework reduces the need for intervention by the user. We also compare skeleton representations with RGB images in a classification task on a large dataset of clothing items, motivating the use of the proposed framework for other classes of garments.
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| 5. |
- Moletta, Marco, et al.
(författare)
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Comparison of Collision Avoidance Algorithms for Autonomous Multi-agent Systems
- 2020
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Ingår i: Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 1-9
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Konferensbidrag (refereegranskat)abstract
- Autonomous multi-agent systems are raising in popularity in recent years. More specifically, Unmanned Aerial Vehicles (UAVs) are involved in modern solutions for surveillance, delivering and film shooting. To carry out these tasks, the avoidance of any possible collision is a crucial matter, mostly when agents need to cooperate. In this paper, different collision avoidance algorithms are compared and analyzed for distributed multi-agent holonomic systems. Our purpose is to identify and clarify the different classes of reciprocal collision avoidance algorithms and then to compare them using meaningful metrics and test for the evaluation.
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