Handling and grasp control with additional grasping point for dexterous manipulation of cylindrical tool

Sugaiwa, Taisuke; Takahashi, Kuniyuki; Kano, Hiroyuki; Iwata, Hiroyasu; Sugano, Shigeki

doi:10.1109/robio.2011.6181373

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…• Adaptability: passive adaptation to environmental changes and safe human-robot interaction with mechanical compliance [1,4,5] CONTACT Kuniyuki Takahashi takahashi@sugano.mech.waseda.ac.jp Supplemental data for this article can be accessed https://doi.org/10.1080/01691864.2017.1383939.…”

Section: Background and Advantages Of Robots With Joint Flexibilitymentioning

confidence: 99%

“…In spite of the above-mentioned benefits of joint flexibility, it is difficult to control flexible-joint robots with a large number of degrees of freedom (DOFs) because of the increased complexity of their dynamics [4]. To address this problem, several approaches have been previously proposed such as model-based control, modelfree machine learning, and motion primitives (oscillators and attractors).…”

Section: Related Research On Flexible Joint Robotsmentioning

confidence: 99%

“…With a model-based control approach, it was demonstrated that a flexible joint robot was able to achieve dynamic motion with passive adaptation to the environment, and was able to generate precise motion such as drawing by reducing the number of effective DOFs using contacts with the environment [4]. Generally speaking, application of model-based approaches would be very challenging on tasks with an increased number of DOFs and considering interactions between the robot and the environment because of the requirement for a precise model description of the complex dynamics of the entire system [4,10]. Therefore, application of such model-based approaches on a complex robot may become significantly difficult in a highly dynamic and uncertain environment particularly including interactions with humans.…”

Section: Related Research On Flexible Joint Robotsmentioning

confidence: 99%

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Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning

et al. 2017

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This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a twophase learning framework of the body dynamics of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a pre-training phase with motor babbling and a fine-tuning phase with additional learning of the target tasks. In the pretraining phase, we consider active and passive exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations.

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Section: Background and Advantages Of Robots With Joint Flexibilitymentioning

confidence: 99%

Section: Related Research On Flexible Joint Robotsmentioning

confidence: 99%