| 1. |
- Arslan Waltersson, Gabriel, 1996, et al.
(författare)
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Planning and Control for Cable-routing with Dual-arm Robot
- 2022
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2022-May, s. 1046-1052
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a new framework for solving cable-routing problems with a dual-arm robot, where the objective is to clip a Deformable Linear Object (DLO) into several arbitrarily placed fixtures. The core of the framework is a task-space planner, which builds a roadmap from predefined tasks and employs a replanning strategy based on a genetic algorithm, if problems occur. The manipulation tasks are executed with either individual or coordinated control of the arms. Moreover, hierarchical quadratic programming is used to solve the inverse differential kinematics together with extra feasibility objectives. A vision system first identifies the desired fixture route and structure preserved registration estimates the state of the DLO in real-time. The framework is tested on real-world experiments with a YuMi robot, demonstrating a 90% success rate for 3 fixture problems.
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| 2. |
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| 3. |
- Laezza, Rita, 1995, et al.
(författare)
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Learning Shape Control of Elastoplastic Deformable Linear Objects
- 2021
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2021-May, s. 4438-4444
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Konferensbidrag (refereegranskat)abstract
- Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need for large amounts of data that can only be satisfied in simulation. This paper proposes a new shape control task for Deformable Linear Objects (DLOs). More notably, we present the first study on the effects of elastoplastic properties on this type of problem. Objects with elastoplasticity such as metal wires, are found in various applications and are challenging to manipulate due to their nonlinear behavior. We first highlight the challenges of solving such a manipulation task from an RL perspective, particularly in defining the reward. Then, based on concepts from differential geometry, we propose an intrinsic shape representation using discrete curvature and torsion. Finally, we show through an empirical study that in order to successfully solve the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward needs to include intrinsic information about the shape of the DLO.
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| 4. |
- Laezza, Rita, 1995, et al.
(författare)
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Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
- 2024
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with approximately linear behavior, they can fail in tasks with more complex dynamics. We investigate an alternative approach, using offline RL to solve a planar shape control problem of a Deformable Linear Object (DLO). To evaluate the effect of material properties, two DLOs are tested namely a soft rope and an elastic cord. We frame this task as a goal-conditioned offline RL problem, and aim to learn to generalize to unseen goal shapes. Data collection and augmentation procedures are proposed to limit the amount of experimental data which needs to be collected with the real robot. We evaluate the amount of augmentation needed to achieve the best results, and test the effect of regularization through behavior cloning on the TD3+BC algorithm. Finally, we show that the proposed approach is able to outperform a shape-servoing baseline in a curvature inversion experiment.
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| 5. |
- Laezza, Rita, 1995, et al.
(författare)
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ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation
- 2021
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1050-4729. - 9781728190778 ; 2021-May, s. 4717-4723
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Konferensbidrag (refereegranskat)abstract
- Recent advances in machine learning have triggered an enormous interest in using learning-based approaches for robot control and object manipulation. While the majority of existing algorithms are evaluated under the assumption that the involved bodies are rigid, a large number of practical applications contain deformable objects. In this work we focus on Deformable Linear Objects (DLOs) which can be used to model cables, tubes or wires. They are present in many applications such as manufacturing, agriculture and medicine. New methods in robotic manipulation research are often demonstrated in custom environments impeding reproducibility and comparisons of algorithms. We introduce ReForm, a simulation sandbox and a tool for benchmarking manipulation of DLOs. We offer six distinct environments representing important characteristics of deformable objects such as elasticity, plasticity or self-collisions and occlusions. A modular framework is used, enabling design parameters such as the end-effector degrees of freedom, reward function and type of observation. ReForm is a novel robot learning sandbox with which we intend to facilitate testing and reproducibility in manipulation research for DLOs.
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| 6. |
- Laezza, Rita, 1995
(författare)
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Robot Learning for Deformable Object Manipulation Tasks
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Deformable Object Manipulation (DOM) is a challenging problem in robotics. Until recently, there has been limited research on the subject, with most robotic manipulation methods being developed with rigid objects in mind. Part of the challenge in DOM is that non-rigid objects require algorithms capable of generalizing to changes in shape as well as different mechanical properties. Machine Learning (ML) has been shown successful in fields, such as computer vision and natural language processing, where generalization is important thus encouraging the application of ML to robotic manipulation. This thesis tackles DOM problems using ML techniques for tasks with Deformable Linear Objects (DLOs), e.g. ropes and cables, found in a variety of industrial applications. DLOs encapsulate a lot of the general challenges in DOM, making them good case studies on the effectiveness of ML for other types of deformable objects. Typically, ML algorithms require large amounts of data that are better satisfied in simulation. Therefore, the ReForm simulation sandbox is introduced, which includes six DLO manipulation tasks. ReForm aims to facilitate comparison and reproducibility of robot learning research on tasks where the goal is to control the shape of a DLO. Such shape control tasks are categorized as: explicit , if a precise shape is to be achieved; or implicit , if its deformation is dictated by a more abstract goal. Two representative DLO manipulation tasks are addressed: (i) shape-servoing (explicit) and (ii) cable-routing (implicit). For shape-servoing, special emphasis is given to Reinforcement Learning (RL) methods. Initial work tackles shape-servoing of an elastoplastic DLO towards a unique goal, using online RL with ReForm. Subsequent work moves towards a multi-goal task in a real-world experimental setup, using offline RL methods to learn directly from real data. In the cable-routing works, the aim is to lay the groundwork for solving this type of task through motion primitives, with limited use of ML. First, a vision-based approach is presented, which is able to route a cable through randomly placed fixtures. Then, a force-based approach is introduced for a similar problem, in which the state and stiffness of a DLO can be estimated through contact with fixtures.
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| 7. |
- Laezza, Rita, 1995
(författare)
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Robot Learning for Manipulation of Deformable Linear Objects
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Deformable Object Manipulation (DOM) is a challenging problem in robotics. Until recently there has been limited research on the subject, with most robotic manipulation methods being developed for rigid objects. Part of the challenge in DOM is that non-rigid objects require solutions capable of generalizing to changes in shape and mechanical properties. Recently, Machine Learning (ML) has been proven successful in other fields where generalization is important such as computer vision, thus encouraging the application of ML to robotics as well. Notably, Reinforcement Learning (RL) has shown promise in finding control policies for manipulation of rigid objects. However, RL requires large amounts of data that are better satisfied in simulation while deformable objects are inherently more difficult to model and simulate. This thesis presents ReForm, a simulation sandbox for robotic manipulation of Deformable Linear Objects (DLOs) such as cables, ropes, and wires. DLO manipulation is an interesting problem for a variety of applications throughout manufacturing, agriculture, and medicine. Currently, this sandbox includes six shape control tasks, which are classified as explicit when a precise shape is to be achieved, or implicit when the deformation is just a consequence of a more abstract goal, e.g. wrapping a DLO around another object. The proposed simulation environments aim to facilitate comparison and reproducibility of robot learning research. To that end, an RL algorithm is tested on each simulated task providing initial benchmarking results. ReForm is one of three concurrent frameworks to first support DOM problems. This thesis also addresses the problem of DLO state representation for an explicit shape control problem. Moreover, the effects of elastoplastic properties on the RL reward definition are investigated. From a control perspective, DLOs with these properties are particularly challenging to manipulate due to their nonlinear behavior, acting elastic up to a yield point after which they become permanently deformed. A low-dimensional representation from discrete differential geometry is proposed, offering more descriptive shape information than a simple point-cloud while avoiding the need for curve fitting. Empirical results show that this representation leads to a better goal description in the presence of elastoplasticity, preventing the RL algorithm from converging to local minima which correspond to incorrect shapes of the DLO.
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| 8. |
- Laezza, Rita, 1995, et al.
(författare)
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Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning
- 2020
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Deformable object manipulation tasks have longbeen regarded as challenging robotic problems. However, untilrecently, very little work had been done on the subject, withmost robotic manipulation methods being developed for rigidobjects. As machine learning methods are becoming morepowerful, there are new model-free strategies to explore forthese objects, which are notoriously hard to model. This paperfocuses on shape control problems for Deformable Linear Objects (DLOs). Despite being one of the most researched classesof DLOs in terms of geometry, no other paper has focusedon materials with elastoplastic properties. Therefore, a novelshape control task, requiring permanent plastic deformationis implemented in a simulation environment. ReinforcementLearning methods are used to learn a continuous controlpolicy. To that end, a discrete curvature measure is usedas a low-dimensional state representation and as part of anintuitive reward function. Finally, three state-of-the-art actor-critic algorithms are compared on the proposed environmentand successfully achieve the goal shape.
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| 9. |
- Süberkrüb, Finn, et al.
(författare)
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Feel the Tension: Manipulation of Deformable Linear Objects in Environments with Fixtures using Force Information
- 2022
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - 2153-0858 .- 2153-0866. - 9781665479271 ; 2022-October, s. 11216-11222
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Konferensbidrag (refereegranskat)abstract
- Humans are able to manipulate Deformable Linear Objects (DLOs) such as cables and wires, with little or no visual information, relying mostly on force sensing. In this work, we propose a reduced DLO model which enables such blind manipulation by keeping the object under tension. Further, an online model estimation procedure is also proposed. A set of elementary sliding and clipping manipulation primitives are defined based on our model. The combination of these primitives allows for more complex motions such as winding of a DLO. The model estimation and manipulation primitives are tested individually but also together in a real-world cable harness production task, using a dual-arm YuMi, thus demonstrating that force-based perception can be sufficient even for such a complex scenario.
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