Robots and robotic systems and methods can employ artificial neural networks (ANNs) to significantly improve performance. The ANNs can operate alternatingly in forward and backward directions in interleaved fashion. The ANNs can employ visible units and hidden units. Various objective functions can be optimized. Robots and robotic systems and methods can execute applications including a plurality of agents in a distributed system, for instance with a number of hosts executing respective agents, at least some of the agents in communications with one another. The hosts can execute agents in response to occurrence of defined events or trigger expressions, and can operate with a maximum latency guarantee and/or data quality guarantee.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of operation of an artificial neural network, including a plurality of variables and a plurality of parameters, the method comprising: computing, by at least one hardware processor, a first plurality of values for the plurality of variables in the artificial neural network based on a first plurality of values for the plurality of parameters over a first period; calculating, by the at least one hardware processor, a first plurality of incremental errors for the artificial neural network over the first period; generating, by the at least one hardware processor, a second plurality of values for the plurality of parameters; storing, by the at least one hardware processor, the second plurality of values for the plurality of parameters; interleaved with calculating the first plurality of incremental errors for the artificial neural network over the first period, computing, over a second period, by the at least one hardware processor, a second plurality of values for the plurality of variables for the artificial neural network based on the first plurality of values for the plurality of parameters; and returning, by the at least one hardware processor, the second plurality of values for the plurality of parameters. 2. The method of claim 1, further comprising: computing, by the at least one hardware processor, over a third period, a third plurality of values for the plurality of variables in the artificial neural network based on the second plurality of values for the plurality of parameters; calculating, by the at least one hardware processor, a second plurality of incremental errors for the artificial neural network over the third period; generating, by the at least one hardware processor, a third plurality of values for the plurality of parameters; and interleaved with calculating the second plurality of incremental errors for the artificial neural network over the third period, computing, by the at least one hardware processor, over a fourth period, a fourth plurality of values for the plurality of variables for the artificial neural network based on the second plurality of values for the plurality of parameters. 3. The method of claim 1, further comprising: storing, by the at least one hardware processor, the first plurality of values for the plurality of variables for the artificial neural network. 4. The method of claim 1 wherein the artificial neural network is associated with an objective function, the method further comprising: computing, by the at least one hardware processor, a gradient for the objective function with respect to the plurality of parameters; and performing, by the at least one hardware processor, gradient descent with the gradient to create the second plurality of values for the plurality of parameters. 5. The method of claim 4 wherein computing the plurality of gradients for the objective function with respect to the parameters comprises: unfolding, by the at least one hardware processor, the artificial neural network over a duration less than or equal to the first period; and performing, by the at least one hardware processor, backpropagation through time on the unfolded artificial neural network. 6. The method of claim 1, further comprising: receiving, by the at least one hardware processor, a plurality of input values for the artificial neural network. 7. The method of claim 1, further comprising: producing, by the at least one hardware processor, a plurality of output values for the artificial neural network; and causing, by the at least one hardware processor, a robot to move based on the plurality of output values. 8. The method of claim 1, further comprising: computing, by the at least one hardware processor for another artificial neural network that includes another plurality of variables and another plurality of parameters, a first plurality of values for the other plurality of variables of the other artificial neural network based on a first plurality of values for the other plurality of parameters; running, by the at least one hardware processor, the other artificial neural network backward over the first period; generating, by the at least one hardware processor, a second plurality of values for the other plurality of parameters; and interleaved with computing, for the other artificial neural the first plurality of values for the other plurality of variables of the other artificial neural network, computing, by the at least one hardware processor, a second plurality of values for the other plurality of variables for the other artificial neural network over the second period. 9. The method of claim 1 wherein behaviors of the artificial neural network are described by a system of differential equations, the method further comprising: numerically integrating, by the at least one hardware processor, the system of differential equations. 10. The method of claim 1 wherein generating the second plurality of values for the plurality of parameters comprises: identifying, by the at least one hardware processor, a system model for a robot; and returning, by the at least one hardware processor, a system model for the robot. 11. A system, comprising: at least one processor; at least one non-transitory computer-readable storage media communicatively coupled to the first processor, and which stores at least one of processor-executable instructions or data thereon which when executed causes the at least one processor to: define an artificial neural network including a plurality of parameters and a plurality of variables; compute, over a first period, a first plurality of values for the plurality of variables based on a first plurality of values for the plurality of parameters; calculate a first plurality of incremental errors for the artificial neural network over the first period; generate a second plurality of values for the plurality of parameters; and interleaved with calculation of the first plurality of incremental errors for the artificial neural network over the first period, compute, over a second period, a second plurality of values for the plurality of variables based on the second plurality of value for the plurality of parameters. 12. The system of claim 11 wherein, when executed, the processor-executable instructions further cause the at least one processor to: compute, over a third period, a third plurality of values for the plurality of variables based on the second plurality of values for the plurality of parameters; calculate a second plurality of incremental errors for the artificial neural network over the third period; generate a third plurality of values for the plurality of parameters; and interleaved with calculation of the second plurality of incremental errors for the artificial neural network over the third period, compute, over a fourth period, a fourth plurality of values for the plurality of variables based on the fourth plurality of values for the plurality of parameters. 13. The system of claim 11 wherein the artificial neural network is associated with an objective function, and when executed, the processor-executable instructions further cause the at least one processor to: compute a gradient for the objective function with respect to the plurality of parameters; and perform gradient descent with the gradient creating the second plurality of values for the plurality of parameters. 14. The system of claim 13 wherein, when executed, the processor-executable instructions further cause the at least one processor to: unfold the artificial neural network over a duration less than or equal to the first period creating an unfolded artificial neural network; and perform backpropagation through time on the unfolded artificial neural network. 15. The system of claim 11, further comprising: a sensor subsystem coupled to the at least one processor; and where, when executed, the processor-executable instructions further cause the at least one processor to: receive a plurality of input values for the artificial neural network from the sensor subsystem. 16. The system of claim 11, further comprising: a robot coupled to the at least one processor; and where, when executed, the processor-executable instructions further cause the at least one processor to: produce a plurality of output values for the artificial neural network; and cause the robot to move based on the plurality of output values. 17. The system of claim 11, further comprising: a network interface subsystem coupled to the at least one processor; and where, when executed, the processor-executable instructions further cause the at least one processor to: send the plurality of variables of the artificial neural network; and send the plurality of parameters of the artificial neural network. 18. The system of claim 11, further comprising: a network interface subsystem coupled to the at least one processor; and where, when executed, the processor-executable instructions further cause the at least one processor to: receive the plurality of variables of the artificial neural network; and receive the plurality of parameters of the artificial neural network. 19.-77. (canceled)