Robust parallel-gripper grasp getection using convolutional neural networks
Texte intégral
Figure
Documents relatifs
parameters: reach duration: the total duration of reaching phase (ms); time to peak velocity: the 134.. ratio between the time at which the maximum velocity occurred
Whereas classical signal processing relies on well founded mathematical properties of Euclidean spaces, we here rely on the algebraic properties of graphs and the deriva- tion of
Nous avons proposé une heuristique constituée de trois phases : (1) la fixation de certaines variables par la résolution heuristique d’un problème de sac à dos avec contraintes
The objective of this problem is to schedule interventions such that the interventions with the highest priority are scheduled at the earliest time possible while satisfying a set
With the Jacquard dataset, we propose a new criterion based on simulation, subsequently called simulated grasp trial-based criterion (SGT). Specifically, when a new grasp should
With generative methods, a DNN is trained to predict one (or multiple ordered) grasps from the sensor input. Predictions can be the direct regression of grasp parameters [6]
Moreover, Proposition 5 and Corollary 4 of [2] establish the orbital stability of this traveling wave as the solution of a variational problem, which is exactly the statement of
The movement and place of articulation features can be used to find the end of the stroke phase. Motions up into the gesture space from rest space or from a