APPARATUSES, COMPUTER-IMPLEMENTED METHODS, AND COMPUTER PROGRAM PRODUCTS FOR CONTINUOUS PERCEPTION DATA LEARNING
Embodiments of the present disclosure provide for improved model training and utilization. Embodiments of the present disclosure utilize high-fidelity data to train individual models across a plurality of computing devices, and utilize high-throughput communications networks (such as 5G communications networks) to enable continuous training of such models and/or central models based on the plurality of individual models (e.g., a federated centralized model optimized based on the individual models). Some embodiments provide the updated central model to one or more computing devices for use in processing further data and/or performing one or more subsequent tasks. In one context, individual AI robots embodying a fleet of AI-driven robots in a particular environment gather various sensor data for use in training updated individual models, and a central learning system aggregates over high-throughput communication network(s) each of the updated individual models to distribute an updated central model trained via a federated learning process.
1 . An apparatus comprising at least one processor and at least one memory, the at least one memory having computer-coded instructions stored thereon that, in execution with the at least one processor, cause the apparatus to:
receive an environment perception data set associated with one or more real-time sensors;
train an updated individual model based at least in part on the environment perception data set; and
transmit, to a central learning system via at least one high-throughput communications network, the updated individual model to cause the real-time data central learning system to update a central model based at least in part on the updated individual model.
2 . The apparatus according to claim 1 , wherein the first computing device receives the environment perception data set via the one or more high-throughput communications network.
3 . The apparatus according to claim 1 , the apparatus further caused to:
receive, from the real-time data central learning system, an updated central model trained based at least in part on the updated individual model and a plurality of other updated individual models associated with a plurality of other computing devices; and
replace the updated individual model with the updated central model.
4 . The apparatus according to claim 1 , wherein the one or more real-time sensors comprises a real-time video sensor, a real-time image sensor, a real-time motion sensor, a real-time location sensor, or a combination thereof.
5 . The apparatus according to claim 1 , the apparatus further configured to:
receive, from the real-time data central learning system, an updated central model trained based at least in part on the updated individual model and a plurality of other updated individual models associated with a plurality of other computing devices;
compare first accuracy data associated with the updated individual model and second accuracy data associated with the updated central model to determine a preferred model representing the updated individual model or the updated central model; and
apply a second environment perception data set to the preferred model.
6 . The apparatus according to claim 1 , the apparatus further configured to:
transmit error data objects associated with the training of the updated individual model to the real-time data central learning system.
7 . The apparatus according to claim 1 , wherein the one or more real-time sensors are each embodied within the first computing device.
8 . The apparatus according to claim 1 , wherein at least one of the one or more real-time sensors is external from the first computing device.
9 . The apparatus according to claim 1 , wherein the updated individual model embodies a reinforcement learning model.
10 . A computer-implemented method comprising:
receiving, at a first computing device, an environment perception data set associated with one or more real-time sensors;
training, at the first computing device, an updated individual model based at least in part on the environment perception data set; and
transmitting, from the first computing device to a central learning system via at least one high-throughput communications network, the updated individual model to cause the real-time data central learning system to update a central model based at least in part on the updated individual model.
11 . The computer-implemented method according to claim 1 , wherein the first computing device receives the environment perception data set via the one or more high-throughput communications network.
12 . The computer-implemented method according to claim 1 , the computer-implemented method further comprising:
receiving, at the first computing device from the real-time data central learning system, an updated central model trained based at least in part on the updated individual model and a plurality of other updated individual models associated with a plurality of other computing devices; and
replacing the updated individual model with the updated central model.
13 . The computer-implemented method according to claim 1 , wherein the one or more real-time sensors comprises a real-time video sensor, a real-time image sensor, a real-time motion sensor, a real-time location sensor, or a combination thereof.
14 . The computer-implemented method according to claim 1 , the computer-implemented method further comprising:
receiving, at the first computing device from the real-time data central learning system, an updated central model trained based at least in part on the updated individual model and a plurality of other updated individual models associated with a plurality of other computing devices;
comparing first accuracy data associated with the updated individual model and second accuracy data associated with the updated central model to determine a preferred model representing the updated individual model or the updated central model; and
applying a second environment perception data set to the preferred model.
15 . The computer-implemented method according to claim 1 , the computer-implemented method further comprising:
transmitting error data objects associated with the training of the updated individual model to the real-time data central learning system.
16 . The computer-implemented method according to claim 1 , wherein the one or more real-time sensors are each embodied within the first computing device.
17 . The computer-implemented method according to claim 1 , wherein at least one of the one or more real-time sensors is external from the first computing device.
18 . The computer-implemented method according to claim 1 , wherein the updated individual model embodies a reinforcement learning model.
19 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon, wherein the computer program code in execution with at least one processor configures the computer program product for:
receiving, at a first computing device, an environment perception data set associated with one or more real-time sensors;
training, at the first computing device, an updated individual model based at least in part on the environment perception data set; and
transmitting, from the first computing device to a central learning system via at least one high-throughput communications network, the updated individual model to cause the real-time data central learning system to update a central model based at least in part on the updated individual model.
20 . The computer program product according to claim 19 , the computer program product further configured for:
receiving, at the first computing device from the real-time data central learning system, an updated central model trained based at least in part on the updated individual model and a plurality of other updated individual models associated with a plurality of other computing devices; and
replacing the updated individual model with the updated central model.