Ball control in high-speed batting motion using hybrid trajectory generator

Senoo, Taku; Namiki, Akio; Ishikawa, Masatoshi

doi:10.1109/robot.2006.1641961

Cited by 73 publications

(42 citation statements)

References 7 publications

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“…Note that our results differ significantly from previous approaches as we use a framework that allows us to learn striking movements from human demonstrations unlike previous work in batting [Senoo et al, 2006] and table tennis [Andersson, 1988]. Unlike baseball which only requires four degrees of freedom (as, e.g., in [Senoo et al, 2006] who used a 4 DoF WAM arm in a manually coded high speed setting), and previous work in table tennis (which had only low-inertia, was overpowered and had mostly prismatic joints [Andersson, 1988, Fässler et al, 1990, Matsushima et al, 2005), we use a full seven degrees of freedom revolutionary joint robot and, thus, have to deal with larger inertia as the wrist adds roughly 2.5k g weight at the elbow. Hence, it was essential to train trajectories by imitation learning that distribute the torques well over the redundant joints as the human teacher was suffering from the same constraints.…”

Section: Playing Against a Ball Launchercontrasting

confidence: 84%

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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Section: Playing Against a Ball Launchercontrasting

confidence: 84%

Learning motor skills: from algorithms to robot experiments

Kober

Peters

2014

It - Information Technology

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“…A body of work has been devoted to autonomous control of fast movements such as catching [3][4][5][6][7][8][9][10], dynamic re-grasping (throwing an object up and catching it) [11], hitting flying objects [12,13] and juggling [14][15][16]. Most approaches assumed a known model of the dynamics of motion and considered solely modeling the translational object motion.…”

Section: Robotic Catchingmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

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a b s t r a c tThis paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass.To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.

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“…A body of work has been devoted to autonomous control of fast movements such as catching [2] [3] [4] [5] [6] [7] and hitting flying objects [8] [9], or juggling [10] [11] [12] [13]. Next, we briefly review these works according to (1) how they predict trajectories of moving objects and (2) how they generate the robot's motions [14].…”

Section: Introductionmentioning

confidence: 99%

“…The coefficients of the polynomial are found by resolving an optimization problem, where the sum of the torques and angular velocities are minimized so as to satisfy constraints on the initial and final position, velocity and acceleration of the end-effector. Senoo et al [8] developed a batting robot based on their high-speed vision system. They split the control of the robot's joint so as to control separately for high-speed swinging while allowing the remaining degrees of freedom are used for fast adaptation to perturbation.…”

Section: Introductionmentioning

confidence: 99%

Learning motion dynamics to catch a moving object

Kim

Gribovskaya

Billard

2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems(DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In previous work of ours [1], we developed an approach to encode robot motion into DS. Such a timeindependent encoding is advantageous in that it offers robustness against violent perturbation by adapting on the fly the trajectory while ensuring high accuracy at the target. We propose here an extension of the system that allows to control the timing of the motion while still benefitting from all the robustness properties deriving from the time-independent encoding of the DS. We validate the approach in experiments where the iCub robot learns from human demonstrations to catch a ball on the fly.

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Ball control in high-speed batting motion using hybrid trajectory generator

Cited by 73 publications

References 7 publications

Learning motor skills: from algorithms to robot experiments

Learning motor skills: from algorithms to robot experiments

Estimating the non-linear dynamics of free-flying objects

Learning motion dynamics to catch a moving object

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