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An impedance control algorithm avoids the tuning of grasping parameters (i.e. grasping velocity and holding force).
Reinforcement learning modifies the connections to and from Affordance Extraction module, resulting in (i) affordances: representations in of combinations of object features relevant for grasping, and (ii) grasp plans: connection weights between the Affordance Extraction and Reach and Grasp Planning modules that bias selection of appropriate grasp motor parameters.
Each of these populations selects grasp motor parameters based on input from AIP and V6A/MIP.
By analyzing the impact of grasping control parameters, base and target mass on the grasping process and combining the life experience, it is found that if the product of speed control parameter and dB adjustment parameter is close to but smaller than the minimum grasping speed, collision impact in the grasping process could be reduced greatly, and then an ideal grasping strategy is proposed.
Procedure, movement recording and data analysis were the same as in experiment 4. In addition, a second series of ANOVAs was carried out on mean values of the reaching-grasping and placing parameters of experiments 4 and 5.
The circles denote regions of grasp parameter space where the model was biased toward selecting grasp parameters that had proved successful with similar objects during training.
Table 1 List of parameters and parameter values.
Then, by selecting appropriate ranges of values for the posture parameters, set of parameters which are the center of the ranges are set to the via posture/grasping posture.
ILGA represents reach and grasp rotation parameters using Euler angles.
As in ILGM, the learned connection weights between the AIP module and the premotor populations encoded grasp parameters most likely to result in the performance of a stable grasp.
His grasp of economics is shaky.
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