Optimization of parametrised kicking motion for humanoid soccer player
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Citations
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
Learning to Use Toes in a Humanoid Robot
Performing the Kick During Walking for RoboCup 3D Soccer Simulation League Using Reinforcement Learning Algorithm
Humanoid Robot Soccer Locomotionand Kick Dynamics: Open Loop Walking, Kicking and Morphing into SpecialMotions on the Nao Robot
References
Motion Planning for Omnidirectional Dynamic Gait in Humanoid Soccer Robots
Combining key frame based motion design with controlled movement execution
Automatic Generation of Humanoid’s Geometric Model Parameters
Humanoid Robot Soccer Locomotionand Kick Dynamics: Open Loop Walking, Kicking and Morphing into SpecialMotions on the Nao Robot
Simultaneous evolution of leg morphology and walking skills to build the best humanoid walker
Related Papers (5)
Frequently Asked Questions (12)
Q2. How many parameters are used to define the kicking trajectory?
Actually the kicking trajectory is defined thanks to a set of three leg configurations defined in the Cartesian space, which makes 12 parameters in total.
Q3. Why did the authors not take into account the x-z parameters of the backward and forward?
The authors did not take into account the x-z parameters of the backward and forward positions because they are assumed to have less influence on the result, and because a reduced set of parameters is better to speed up the optimization process.
Q4. What is the objective of the smooth optimization of expert parameters?
Since the objective consists of finding stable moves, the authors believe that the smooth optimization of expert parameters is a promising policy.
Q5. What can be done to modify the lateral position of the toe?
However the lateral position of the toe can be adjustedto modify the kicking direction so as to hit the ball in the center, which can provide more flexibility to the kicking motion.
Q6. What is the function that compares with ′?
Inside the Test function, the pickOut function compares ν with ν′ and returns three possible values BESTAlgorithm 1 evolution < T > (k, L, pickOut) 1: ν′ ← expertKnowledge 2: H ← ∅ 3: while not-interrupted do 4: p← Generate < T > (H, L) 5: ν ← multipleTrials < T > (p, k) 6: (ν′, H) ← Test < T > (ν, ν′, p, H, pickOut) 7: end while 8: return paramsFrom < T > (ν′)(which implies ν′ ← ν), EQUAL and WORST.
Q7. What is the effect of the kicking foot?
This enables to increase the velocity of the kicking foot at the time of hitting the ball, therefore transmitting a larger amount of kinetic energy to the ball, which permits to send the ball farther away.
Q8. How much is the lateral deviation of the ball?
By using C2 parameters, the lateral deviation of the ball can be reduced as the related standard deviation is 2 times less than C1, and 4.7 times less than C3 results.
Q9. What is the definition of a parametrised kick?
The parametrised kick consists of the following phases (Fig. 1): • sway hips to transfer the load above the kicking foot,then lift, swing, and put down the supporting foot.
Q10. What is the time elapsed for the evolution process?
When the time elapsed is considered as sufficient to produce an interesting solution, the evolution process is instantaneously interrupted and the resulting input parameters are returned.
Q11. What were the positions of the toe in the backward and forward positions?
The positions of the toe in the backward and the forward positions were not used as evolving parameters in the optimization process.
Q12. What is the way to optimize a smooth problem?
This iterative maximum-optimization process has been experimentally shown to be less time consuming than classical regression methods for smooth problem optimization; it does not need any reference execution as samples are iteratively selected according to their average win rate.