Methods and apparatus for early sensory integration and robust acquisition of real world knowledge
The systems and methods disclosed herein include a path integration system that calculates optic flow, infers angular velocity from the flow field, and incorporates this velocity estimate into heading calculations. The resulting system fuses heading estimates from accelerometers, 5 gyroscopes, engine torques, and optic flow to determine self-localization. The system also includes a motivational system that implements a reward drive, both positive and negative, into the system. In some implementations, the drives can include: a) a curiosity drive that encourages exploration of new areas, b) a resource drive that attracts the agent towards the recharging base when the battery is low, and c) a mineral reward drive that attracts the agent 10 towards previously explored scientific targets.
1. A system for training a neural network in a virtual environment before deploying the neural network on a first platform, the system comprising:
a virtual engine to simulate sensory information using a simulated platform;
a proxy engine to:
transmit the simulated sensory information to the neural network;
extract neural information from the neural network based on the simulated sensory information; and
transmit control information to the simulated platform based on the extracted neural information; and
an Application Programming Interface, communicatively coupled to the virtual engine and the proxy engine, to enable communication between the neural network and the simulated platform,
wherein the virtual engine and the proxy engine system are configured to test the simulated platform.
2. The system of claim 1 , wherein the Application Programming Interface is further configured to deploy the neural network to the first platform.
3. The system of claim 2 , wherein the first platform is a robotic platform.
4. The system of claim 1 , wherein the simulated platform includes at least one animat controlled by the neural network model.
5. The system of claim 4 , wherein the at least one animat includes a plurality of simulated sensory organs and a plurality of animat controls.
6. The system of claim 1 , further comprising:
a graphical user interface configured to enable a user to the control the system.
7. The system of claim 6 , wherein the graphical user interface is further configured to enable a user to create at least one animat controlled by the neural network model.
8. A system for training a neural network in a virtual environment before deploying the neural network on a first platform, the system comprising:
a virtual engine to simulate sensory information using a simulated platform;
a proxy engine to:
transmit the simulated sensory information to the neural network;
extract neural information from the neural network based on the simulated sensory information; and
transmit control information to the simulated platform based on the extracted neural information;
an Application Programming Interface, communicatively coupled to the virtual engine and the proxy engine, to enable communication between the neural network and the simulated platform; and
a simulation engine to generate the simulated platform.
9. A method for training a neural network in a virtual environment before deploying the neural network on a first platform, the method comprising:
simulating, via a virtual engine, sensory information using a simulated platform;
transmitting, via a proxy engine, the simulated sensory information to the neural network;
extracting, via the proxy engine, neural information from the neural network based on the simulated sensory information;
transmitting, via the proxy engine, control information to the simulated platform based on the extracted neural information; and
testing, via the virtual engine and the proxy engine, the simulated platform.
10. The method of claim 9 , further comprising:
deploying, via an Application Programming Interface, the neural network to the first platform.
11. The method of claim 10 , wherein the first platform is a robotic platform.
12. The method of claim 9 , further comprising:
controlling, via the neural network, at least one animat included in the simulated platform.
13. The method of claim 12 , wherein the at least one animat includes a plurality of simulated sensory organs and a plurality of animat controls.
14. A method for training a neural network in a virtual environment before deploying the neural network on a first platform, the method comprising:
generating, via a simulation engine, a simulated platform;
simulating, via a virtual engine, sensory information using the simulated platform;
transmitting, via a proxy engine, the simulated sensory information to the neural network;
extracting, via the proxy engine, neural information from the neural network based on the simulated sensory information; and
transmitting, via the proxy engine, control information to the simulated platform based on the extracted neural information.
15. The method of claim 14 , further comprising:
deploying, via an Application Programming Interface, the neural network to the first platform.
16. The method of claim 15 , wherein the first platform is a robotic platform.
17. The method of claim 14 , further comprising:
controlling, via the neural network, at least one animat included in the simulated platform.
18. The method of claim 17 , wherein the at least one animat includes a plurality of simulated sensory organs and a plurality of animat controls.
19. The system of claim 8 , wherein the Application Programming Interface is further configured to deploy the neural network to the first platform.
20. The system of claim 19 , wherein the first platform is a robotic platform.
21. The system of claim 8 , wherein the simulated platform includes at least one animat controlled by the neural network model.
22. The system of claim 21 , wherein the at least one animat includes a plurality of simulated sensory organs and a plurality of animat controls.
23. The system of claim 8 , further comprising:
a graphical user interface configured to enable a user to the control the system.
24. The system of claim 23 , wherein the graphical user interface is further configured to enable a user to create at least one animat controlled by the neural network model.