Development Of A Pingpong Robot System Using 7 Degrees Of Freedom Direct Drive Arm

Hashimoto, Hajime; Ozaki, Fumio; Osuka, K.

doi:10.1117/12.943016

Cited by 34 publications

(21 citation statements)

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“…7b. 8 While the impact of the vision errors is quite evident from the plot, both plots evidence a similar plateau effect to that observed in the CrKR performance. The plateau effect appears sooner when using the vision system, but is also visible when using ground-truth values for s ball and ṡ ball .…”

Section: Discussionsupporting

confidence: 49%

Towards table tennis with a quadrotor autonomous learning robot and onboard vision

Silva

Melo

Veloso

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Robot table tennis is a challenging domain in both robotics, artificial intelligence and machine learning. In terms of robotics, it requires fast and reliable perception and control; in terms of artificial intelligence, it requires fast decision making to determine the best motion to hit the ball; in terms of machine learning, it requires the ability to accurately estimate where and when the ball will be so that it can be hit. The use of sophisticated perception (relying, for example, in multicamera vision systems) and state-of-the-art robot manipulators significantly alleviates concerns with perception and control, leaving room for the exploration of novel approaches that focus on estimating where, when and how to hit the ball. In this paper, we move away from the hardware setup commonly used in this domain-typically relying on robotic manipulators combined with an array of multiple fixed cameras-and give the first steps towards having autonomous aerial table tennis robotic players. Specifically, we focus on the task of hitting a ping pong ball thrown at a commercial drone, equipped with a light cardboard racket and an onboard camera. We adopt a general framework for learning complex robot tasks and show that, in spite of the perceptual and actuation limitations of our system, the overall approach enables the quadrotor system to successfully respond to balls served by a human user.

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Section: Discussionsupporting

confidence: 49%

Towards table tennis with a quadrotor autonomous learning robot and onboard vision

Silva

Melo

Veloso

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The transfer function is given Appendix F -Review of Robot Table Tennis Driven by a robot-table tennis competition series, several early systems were presented between 1983 and 1993 (Andersson, 1988;Knight and Lowery, 1986;Hartley, 1987;Hashimoto et al, 1987;Fässler et al, 1990). The focus in that period was mainly on the design of efficient vision systems and improved hardware rather than finding robust algorithms to achieve a performance comparable to humans.…”

Section: Appendix Appendix a -Index To Multimedia Extensions Appendixmentioning

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

343

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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“…[4], [5]; and the other of which is based on explicit physical models, e.g. [6], [7], [8], [9]. In these studies, the rotational velocity was not considered which effects on the ball trajectory greatly because its magnitude is very large, 3000 [rpm] and the mass of a table tennis ball is very light, 2.7 [g] [3].…”

Section: Introductionmentioning

confidence: 99%

A learning method for returning ball in robotic table tennis

Nakashima

Takayanagi

Hayakawa

2014

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

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A learning method of the point for a robot to hit a coming ball in table tennis is proposed in this paper. The learning is performed based on the artificial neural network. In order to learn the effects of the rotational velocity and the air resistance, the inputs and outputs are defined as the variations of the measured data and the hitting point from those produced by a simple model, which consists of the equations of motion without the air resistance and the Newton's rebound model without friction. The learning and verification are performed using the simulation and experimental data, where the simulation is executed with the aerodynamics model and the table rebound model, and the ball trajectories in the experiment are measured when two humans play table tennis.

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Development Of A Pingpong Robot System Using 7 Degrees Of Freedom Direct Drive Arm

Cited by 34 publications

References 0 publications

Towards table tennis with a quadrotor autonomous learning robot and onboard vision

Towards table tennis with a quadrotor autonomous learning robot and onboard vision

Learning to select and generalize striking movements in robot table tennis

A learning method for returning ball in robotic table tennis

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