Dimensionality Reduction for Dynamic Movement Primitives and Application to Bimanual Manipulation of Clothes

Colomé, Adrià; Torras, Carme

doi:10.1109/tro.2018.2808924

Cited by 56 publications

(50 citation statements)

References 29 publications

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“…Deep learning was used in [31] and tested on a single piece of rectangular garment too. A t-shirt was folded in work [32]. Authors combined learning from demonstration with dimensionality reduction to learn a folding task parametrized by dynamic movement primitives.…”

Section: Learning Based Approachesmentioning

confidence: 99%

Static Stability of Robotic Fabric Strip Folding

Petrík

Smutný

Kyrki

2020

IEEE/ASME Trans. Mechatron.

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Planning accurate manipulation for deformable objects requires prediction of their state. The prediction is often complicated by a loss of stability that may result in collapse of the deformable object. In this work, stability of a fabric strip folding performed by a robot is studied. We show that there is a static instability in the folding process. This instability is detected in a physics-based simulation and the position of the instability is verified experimentally by real robotic manipulation. Three state-of-the-art methods for folding are assessed in the presence of static instability. It is shown that one of the existing folding paths is suitable for folding of materials with internal friction such as fabrics. Another folding path that utilizes dynamic motion exists for ideal elastic materials without internal friction. Our results show that instability needs to be considered in planning to obtain accurate manipulation of deformable objects.

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Section: Learning Based Approachesmentioning

confidence: 99%

Static Stability of Robotic Fabric Strip Folding

Petrík

Smutný

Kyrki

2020

IEEE/ASME Trans. Mechatron.

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“…In other words, motor skills must be selected from a library of multiple ones. However, many researchers have focused on dealing with a single motor skill [37][38][39][40]. They suggested the ways to improve the performance of a motor skill.…”

Section: Discussionmentioning

confidence: 99%

Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

et al. 2020

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We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: "hole search" and "peg insertion". The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally. of motor skills. However, for acquiring complete motor skills, it has one evident limitation: it does not ensure that robots acquire motor skills that are optimized for their goals (that is, it generally provides near-optimal solutions) [5]. Furthermore, it is not easy for human performers to provide a demonstration dataset that can cover all situations arising during the execution of a motor skill [6]. Nonetheless, these human demonstrations can be used as a solid starting point for robots to acquire motor skills [7]. To obtain optimal motor skills, robots must be able to improve motor skills through self-learning. However, this self-learning is a time-consuming and expensive process in the absence of references. We attempt to obtain these optimal solutions with fewer trials-and-errors by providing near-optimal solutions learned from human demonstrations. In this paper, robots improve motor skills to optimize them-referred to as improvement-and generalize them so that they are widely applicable-known as generalization-through self-learning.The peg-in-hole task has also been addressed through several imitation learning studies [8,9]. However, the peg-in-hole task is not easy to learn with this method alone. The main reason is that it is difficult for human performers to provide a complete demonstration dataset to robots, because it is not feasible to prepare all possible reaction situations. In addition, unintended reaction information may be included in the dataset during the demonstration process. These problems may prevent robots from acquiring the complete motor skills. Thus, initial motor skills are learned to classify reaction force/moment signals and generate reaction motion trajectories from human demonstrations, and thei...

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“…In [26] they used an open-loop procedure to fold a Tshirt where clearly the sleeves where not correctly folded. A more informed method was used in [27] with reinforcement learning, assuming a piece already grasped and without performing the release. Attempting this operation with only point contacts (a pinch grasp) is very challenging and requires a lot of dynamics.…”

Section: B Task 2: Fold a T-shirt In The Airmentioning

confidence: 99%

A Versatile Gripper for Cloth Manipulation

Donaire

Borràs

Alenyà

et al. 2020

IEEE Robot. Autom. Lett.

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Cloth manipulation has been mostly advancing in perception and modeling methods for cloth state estimation and grasping point detection. In comparison, less attention has been put on end-effector design. Indeed, most implementations use a parallel gripper that can only perform pinch grasps. Instead, a more versatile set of possible grasp types could ease many tasks by providing more support to certain parts of the object, as well as make feasible tasks that become very complex when executed with only pinch grasps. We present a versatile gripper design which, besides the common open-close thumb+finger(s) feature, it has a couple of reconfiguration degrees of freedom that offer, first, a wide base plane to provide support and, second, variable friction surface on the thumb tip. Our gripper can execute a versatile set of grasps that ease some complex tasks such as pick and place folded clothes or fold in the air. In addition, the variable friction mechanism enables a more robust pinch-and-slide manipulation to trace cloth edges. Our evaluation shows the gripper potential to execute a wide variety of cloth manipulation tasks.

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Dimensionality Reduction for Dynamic Movement Primitives and Application to Bimanual Manipulation of Clothes

Cited by 56 publications

References 29 publications

Static Stability of Robotic Fabric Strip Folding

Static Stability of Robotic Fabric Strip Folding

Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

A Versatile Gripper for Cloth Manipulation

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