US 12,436,531 B2
Augmented learning model for autonomous earth-moving vehicles
Devin Lu, Sunnyvale, CA (US); and Thomas Wei, Plano, TX (US)
Assigned to AIM Intelligent Machines, Inc., Redmond, WA (US)
Filed by AIM Intelligent Machines, Inc., Monroe, WA (US)
Filed on Sep. 7, 2023, as Appl. No. 18/463,185.
Claims priority of provisional application 63/500,227, filed on May 4, 2023.
Prior Publication US 2024/0370016 A1, Nov. 7, 2024
Int. Cl. G05D 1/00 (2024.01); G06N 20/00 (2019.01); B66C 13/48 (2006.01); E02F 9/20 (2006.01)
CPC G05D 1/0088 (2013.01) [G06N 20/00 (2019.01); B66C 13/48 (2013.01); E02F 9/205 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method for autonomous operation of a vehicle, comprising:
(a) maintaining, at a computer data store, a first set of sensor data;
(b) until a convergence condition is reached:
(i) executing, by one or more processors, a first set of instructions and a second set of instructions in parallel, the first set of instructions comprising:
(1) receiving a second set of sensor data, wherein the second set of sensor data is not included within the first set of sensor data;
(2) generating a first condensed vector from the second set of sensor data at least in part by processing the second set of sensor data with a first machine learning model; and
(3) selecting an action to be performed by the vehicle at least in part by processing the first condensed vector with a second machine learning model;
(ii) the second set of instructions comprising:
(1) retrieving one or more samples of sensor data from the first set of sensor data and/or the second set of sensor data;
(2) fine-tuning the first machine learning model at least in part by processing the one or more samples of sensor data to produce a second condensed vector; and
(3) fine-tuning the second machine learning model at least in part by processing the second condensed vector; and
(c) prior to (b), (i) training, by the one or more processors, the first machine learning model on the first set of sensor data and (ii) training, by the one or more processors, the second machine learning model at least in part on the first condensed vector produced by the first machine learning model.