Flower Stick Manipulation Based on the High-speed Visual Feedback Control of a Dual Robotic Arm

Aoyama, Tadayoshi; Matsuba, Kenta; Takaki, Takeshi; Ishii, Idaku; Hasegawa, Yasuhisa

doi:10.1016/j.ifacol.2018.11.543

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Devilstick manipulation with a mechanical aid and 3ball juggling was also realized by hydraulic robotic arms by Schaal et al [14], [15]. Flowerstick (similar to devilstick) manipulation was also addressed with visual feedback control using a high-speed vision system [16]. Contact juggling was simplified to a disk-on-disk system and demonstrated with closed loop [17] or feedback stabilization control in [18], [19].…”

Section: A Juggling With Robotsmentioning

confidence: 99%

An analytical diabolo model for robotic learning and control

Drigalski

Joshi

Murooka

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a diabolo model that can be used for training agents in simulation to play diabolo, as well as running it on a real dual robot arm system. We first derive an analytical model of the diabolo-string system and compare its accuracy using data recorded via motion capture, which we release as a public dataset of skilled play with diabolos of different dynamics. We show that our model outperforms a deep-learning-based predictor, both in terms of precision and physically consistent behavior. Next, we describe a method based on optimal control to generate robot trajectories that produce the desired diabolo trajectory, as well as a system to transform higher-level actions into robot motions. Finally, we test our method on a real robot system by playing the diabolo, and throwing it to and catching it from a human player.

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Section: A Juggling With Robotsmentioning

confidence: 99%

An analytical diabolo model for robotic learning and control

Drigalski

Joshi

Murooka

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Real-time motion force-feedback system with predictive-vision for improving motor accuracy

Matsui,

Aoyama,

Kato

et al. 2024

Sci Rep

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Many haptic guidance systems have been studied over the years; however, most of them have been limited to predefined guidance methods. Calculating guidance according to the operator’s motion is important for efficient human motor adaptation and learning. In this study, we developed a system that haptically provides guidance trajectory by sequential weighting between the operator’s trajectory and the ideal trajectory calculated from a predictive-vision system. We investigated whether motion completion with a predictive-vision system affects human motor accuracy and adaptation in time-constrained goal-directed reaching and ball-hitting tasks through subject experiments. The experiment was conducted with 12 healthy participants, and all participants performed ball-hitting tasks. Half of the participants get forceful guidance from the proposed system in the middle of the experiment. We found that the use of the proposed system improved the operator’s motor performance. Furthermore, we observed a trend in which the improvement in motor performance using this system correlated with that after the washout of this system. These results suggest that the predictive-vision system effectively enhances motor accuracy to the target error in dynamic and time-constrained reaching and hitting tasks and may contribute to facilitating motor learning.

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