An Offline Large Language Model for Drone Control and Monitoring is disclosed. The system incorporates a smaller large language model that is trained in a much similar way, but in an offline setting to simplify drone operation, making it accessible to users with minimal training. This is especially advantageous in military contexts where quick deployment and ease of use are critical. The offline nature of the model ensures functionality in environments without reliable internet access.

A system includes a processor that collects and analyzes meteorological data and sensor data in real time, predicts future disaster risks based on past disaster data, detects abnormal patterns and identifies precursors of disasters, automatically issues warnings based on identified precursors, calculates optimal evacuation routes and provides them to users in real time, and supports information exchange between affected areas and relief teams.

A system includes a processor that collects and analyzes meteorological data and sensor data in real time, predicts future disaster risks based on past disaster data, detects abnormal patterns and identifies precursors of disasters, automatically issues warnings based on identified precursors, calculates optimal evacuation routes and provides them to users in real time, and supports information exchange between affected areas and relief teams.

In various examples, a technique for performing end-to-end navigation using a generative world model includes converting a set of sensory inputs received by a machine at a current time step into a set of embedded features. The technique also includes generating, via execution of one or more neural networks, one or more states associated with the current time step based at least on the set of embedded features, a history of states preceding the current time step, and a first set of actions associated with a previous time step. The technique further includes converting, via execution of the one or more neural networks, the one or more states into a set of predictions associated with the current time step, and performing, by the machine, a second set of actions associated with the current time step based on the set of predictions.

In various examples, a technique for performing end-to-end navigation using a generative world model includes converting a set of sensory inputs received by a machine at a current time step into a set of embedded features. The technique also includes generating, via execution of one or more neural networks, one or more states associated with the current time step based at least on the set of embedded features, a history of states preceding the current time step, and a first set of actions associated with a previous time step. The technique further includes converting, via execution of the one or more neural networks, the one or more states into a set of predictions associated with the current time step, and performing, by the machine, a second set of actions associated with the current time step based on the set of predictions.

A method for controlling an agent. The method includes determining, for a present state of the agent and a state of an environment of the agent in which the agent should be controlled, a control history indicating a sequence of actions performed by the agent that led to the present state and indicating observations about changes of a state of the agent and/or a state of an environment of the agent, determining an encoding of the control history by supplying the control history to a history encoder comprising a Kalman filter, wherein the encoding is given by a system state estimate determined by the Kalman filter, supplying the encoding to a control policy trained to determine actions from control policy encodings and controlling the agent to perform an action provided by the control policy in response to being supplied with the encoding.

A method for controlling an agent. The method includes determining, for a present state of the agent and a state of an environment of the agent in which the agent should be controlled, a control history indicating a sequence of actions performed by the agent that led to the present state and indicating observations about changes of a state of the agent and/or a state of an environment of the agent, determining an encoding of the control history by supplying the control history to a history encoder comprising a Kalman filter, wherein the encoding is given by a system state estimate determined by the Kalman filter, supplying the encoding to a control policy trained to determine actions from control policy encodings and controlling the agent to perform an action provided by the control policy in response to being supplied with the encoding.

A method for performing a robot maneuver includes outputting, via one or more processors of a controller, instructions to at least a robot or an actuating device separate from the robot, wherein the instructions cause the robot to execute a maneuver. The method also includes obtaining, via the one or more processors of the controller, at least position information or orientation information for the robot subsequent to outputting the instructions. Further, the method includes determining, via the one or more processors of the controller, that the maneuver is executed unsuccessfully based on at least the position information or the orientation information. Further still, the method includes adjusting, via the one or more processors of the controller, operation of at least the robot or the actuating device separate from the robot based on the maneuver being executed unsuccessfully.

A method for performing a robot maneuver includes outputting, via one or more processors of a controller, instructions to at least a robot or an actuating device separate from the robot, wherein the instructions cause the robot to execute a maneuver. The method also includes obtaining, via the one or more processors of the controller, at least position information or orientation information for the robot subsequent to outputting the instructions. Further, the method includes determining, via the one or more processors of the controller, that the maneuver is executed unsuccessfully based on at least the position information or the orientation information. Further still, the method includes adjusting, via the one or more processors of the controller, operation of at least the robot or the actuating device separate from the robot based on the maneuver being executed unsuccessfully.

A method of controlling a powered lift aircraft comprises receiving, from at least one pilot input device, an input indicative of a landing of the powered lift aircraft, controlling, using at least one control circuit, at least one aircraft powered lift element to operate based on the received input. When the input is indicative of a vertical landing, controlling comprises allowing the at least one aircraft powered lift element to provide powered lift to the aircraft based on a sensed airspeed of the aircraft and when the input is indicative of a non-vertical landing, controlling comprises preventing the at least one aircraft powered lift element from providing powered lift to the aircraft.