Grasping with Vision Descriptors and Motor Primitives

Kroemer, Oliver; Detry, Renaud; Piater, Justus; Peters, Jan

doi:10.1007/978-3-642-19539-6_14

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…However, if additional object or joint limit avoidance is necessary, the movement could be adapted by adding a repellent force. In this case, a potential field centered around the obstacle (as described by Park et al (2008) and Kroemer et al (2010)) could be used. Luckily, these security precautions were never required in our experiments in practice as the MoMP generates movements within the convex combination of demonstrations.…”

Section: Algorithm 3 Imitation Learning Of One Dmp For Mompmentioning

confidence: 99%

Learning to select and generalize striking movements in robot table tennis

Mülling

Kober

Kroemer

et al. 2013

The International Journal of Robotics Research

Self Cite

342

248

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Learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning are needed. In this paper, we take the task of learning table tennis as an example and present a new framework that allows a robot to learn cooperative table tennis from physical interaction with a human. The robot first learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a set of motor primitives represented by dynamical systems. The robot subsequently generalizes these movements to a wider range of situations using our mixture of motor primitives approach. The resulting policy enables the robot to select appropriate motor primitives as well as to generalize between them. Finally, the robot plays with a human table tennis partner and learns online to improve its behavior. We show that the resulting setup is capable of playing table tennis using an anthropomorphic robot arm.

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Section: Algorithm 3 Imitation Learning Of One Dmp For Mompmentioning

confidence: 99%

Learning to select and generalize striking movements in robot table tennis

Mülling

Kober

Kroemer

et al. 2013

The International Journal of Robotics Research

Self Cite

342

248

View full text Add to dashboard Cite

show abstract

“…In this context, even slight uncertainties can lead to object slippage and failed grasps. To improve the robustness of vision-driven grasping, Krömer et al (2010a) augmented DMPs with a potential field based on visual descriptors to adapt hand and finger trajectories according to the local geometry of the object. This grasping strategy was integrated within a hierarchical control architecture where the upper level determines the object’s grasp location and the lower level locally adjusts the motion to achieve a robust grasp of the object (Krömer et al, 2010b).…”

Section: Dmps Integration In Complex Frameworkmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives’ theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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“…In this setting, even small uncertainties may cause the object to drop and the grasp to fail. To improve the robustness of vision driven grasping, Krömer et al (2010a) augmented DMPs with a potential field based on visual descriptors that adapts hand and finger trajectories to the object's local geometry. This grasping strategy was integrated in a hierarchical control architecture where the upper level decides where to grasp the object and the lower level locally adapted the motion to robustly grasp the object (Krömer et al 2010b).…”

Section: Manipulation Tasksmentioning

confidence: 99%

Dynamic Movement Primitives in Robotics: A Tutorial Survey

Saveriano¹,

Abu‐Dakka²,

Kramberger³

et al. 2021

Preprint

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Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally lead to the formulation of the motor primitives theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems, and are well suited to generate motor commands for artificial systems like robots. In the last decades, Dynamic Movement Primitives (DMPs) have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human-robot co-working, resulting a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMPs formulations in rigorous mathematical terms, and discuss advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state of the art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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Grasping with Vision Descriptors and Motor Primitives

Cited by 3 publications

References 14 publications

Learning to select and generalize striking movements in robot table tennis

Learning to select and generalize striking movements in robot table tennis

Dynamic movement primitives in robotics: A tutorial survey

Dynamic Movement Primitives in Robotics: A Tutorial Survey

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