Bouncing an Unconstrained Ball in Three Dimensions with a Blind Juggling Robot

Reist, Philipp; D’Andrea, Raffaello

doi:10.1109/robot.2009.5152616

Cited by 27 publications

(26 citation statements)

References 21 publications

(30 reference statements)

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“…Though the regularization constraints shown in (15) and (16) are effective at addressing these issues, they require human supervision. We plan to explore alternative shape representations that reduce dimensionality, and to develop heuristics for finding more natural regularization constraints of a given task.…”

Section: Discussionmentioning

confidence: 99%

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Optimal Shape and Motion Planning for Dynamic Planar Manipulation

Taylor

Rodríguez

2017

Robotics: Science and Systems XIII

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Abstract-This paper presents a framework for optimizing both the shape and the motion of a planar rigid end-effector to satisfy a desired manipulation task. We frame this design problem as a nonlinear optimization program, where shape and motion are decision variables represented as splines. The task is represented as a series of constraints, along with a fitness metric, which force the solution to be compatible with the dynamics of frictional hard contact while satisfying the task.We illustrate the approach with the example problem of moving a disk along a desired path or trajectory, and we verify it by applying it to three classical design problems: the rolling brachistochrone, the design of teeth of involute gears, and the pitch curve of rolling cams. We conclude with a case study involving the optimization and real implementation of the shape and motion of a dynamic throwing arm.

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Section: Discussionmentioning

confidence: 99%

“…Reist and D'Andrea. [16,17] optimize the motion and concavity of a paddle juggler capable of stably bouncing a ball without feedback. The approach is limited to the particular application, and the contact manipulation is limited to periodic instantaneous impacts.…”

Section: Related Workmentioning

confidence: 99%

Optimal Shape and Motion Planning for Dynamic Planar Manipulation

Taylor

Rodríguez

2017

Robotics: Science and Systems XIII

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“…Furthermore, negative acceleration seems desirable, as it was shown to lead to stable juggling cycles [13]. However, instead of a parabola we select a sinusoidal excitation motion of the hand, since during contact the considered system is modeled by a second order mass-spring dynamic.…”

Section: A Hand Reference Trajectory For 1 Dof Modelmentioning

confidence: 99%

“…In [12] the first blindly juggling robot was presented. [13] used only a linear motor for juggling without the need of active ball tracking or other feedback, as the lateral motion is stabilized by the shape of the juggling paddle. In [13] the authors compared an H 2 optimal controller with the previous openloop solution, which turned out to have similar performance characteristics.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Elastic Energy Storage for “Blind” Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling

Haddadin

Krieger²,

Albu‐Schäffer

et al. 2018

IEEE Trans. Robot.

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Abstract-For creating robots that are capable of human like performance in terms of speed, energetic properties and robustness, intrinsic compliance is a promising design element. In this paper we investigate the principle effects of elastic energy storage and release for basketball dribbling in terms of open-loop cycle stability. We base the analysis, which is performed for the 1-degree-of-freedom (DoF) case, on error propagation, peak power performance during hand contact and robustness with respect to varying hand stiffness. As the ball can only be controlled during contact, an intrinsically elastic hand extends the contact time and improves the energetic characteristics of the process. To back up our basic insights, we extend the 1-DoF controller to 6-DoFs and show how passive compliance can be exploited for a 6-DoF cyclic ball dribbling task with a 7-DoF articulated Cartesian impedance controlled robot. As a human is able to dribble blindly, we decided to focus on the case of contact force sensing only, i.e. no visual information is used in our approach. We show via simulation and experiment that it is possible to achieve a stable dynamic cycle based on the 1 DoF analysis for the primary vertical axis together with control strategies for the secondary translations and rotations of the task. The scheme allows also the continuous tracking of a desired dribbling height and horizontal position. The approach is also used to hypothesize about human dribbling and is validated with captured data.

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“…However, this method requires the knowledge of the ball position and velocity, as well as a model of the ball behavior. An alternative strategy that stabilizes the behavior in a completely open loop behavior by employing a slightly concave paddle shape has been suggested in [Reist and D'Andrea, 2009]. A method similar to the proposed strategy has been employed by Kulchenko [2011] and Müller et al [2011].…”

Section: Bouncing Strategymentioning

confidence: 99%

Learning motor skills: from algorithms to robot experiments

Kober

Peters

2014

It - Information Technology

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Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 2.0 Deutschland http://creativecommons.org/licenses/by-nc-nd/2.0/de/ Abstract Ever since the word "robot" was introduced to the English language by KarelČapek's play "Rossum's Universal Robots" in 1921, robots have been expected to become part of our daily lives. In recent years, robots such as autonomous vacuum cleaners, lawn mowers, and window cleaners, as well as a huge number of toys have been made commercially available. However, a lot of additional research is required to turn robots into versatile household helpers and companions. One of the many challenges is that robots are still very specialized and cannot easily adapt to changing environments and requirements. Since the 1960s, scientists attempt to provide robots with more autonomy, adaptability, and intelligence. Research in this field is still very active but has shifted focus from reasoning based methods towards statistical machine learning. Both navigation (i.e., moving in unknown or changing environments) and motor control (i.e., coordinating movements to perform skilled actions) are important sub-tasks.In this thesis, we will discuss approaches that allow robots to learn motor skills. We mainly consider tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The presented tasks correspond to sports and games but the presented techniques will also be applicable to more mundane household tasks. Motor skills can often be represented by motor primitives. Such motor primitives encode elemental motions which can be generalized, sequenced, and combined to achieve more complex tasks. For example, a forehand and a backhand could be seen as two different motor primitives of playing table tennis. We show how motor primitives can be employed to learn motor skills on three different levels. First, we discuss how a single motor skill, represented by a motor primitive, can be learned using reinforcement learning. Second, we show how such learned motor primitives can be generalized to new situations. Finally, we present first steps towards using motor primitives in a hierarchical setting and how several motor primitives can be combined to achieve more complex tasks.To date, there have been a number of successful applications of learning motor primitives employing imitation learning. However, many interesting motor learning problems are high-dimensional reinforcement learning problems which are often beyond the reach of current reinforcement learning methods. We review research on reinforcement learning applied to robotics and point out key challenges and important strategies to render reinforcement learning tractable. Based on these insights, we introduce novel learning approaches both for single and generalized motor skills.For learning single motor skills, we study parametrized policy search methods and introduce a framework of reward-weighted imi...

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Bouncing an Unconstrained Ball in Three Dimensions with a Blind Juggling Robot

Cited by 27 publications

References 21 publications

Optimal Shape and Motion Planning for Dynamic Planar Manipulation

Optimal Shape and Motion Planning for Dynamic Planar Manipulation

Exploiting Elastic Energy Storage for “Blind” Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling

Learning motor skills: from algorithms to robot experiments

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