Baidu USA LLC

Baidu Online Network Technology (beijing) Co., Ltd.

Jinxin Zhao

Liangjun Zhang

Liyang Wang

Ruigang Yang

He Jiang

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

Engineering machinery equipment, and method, system, and storage medium for safety control thereof

A scalable solution to robot behavioral navigation following natural language instructions is presented. An example of the solution includes: receiving, by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot to navigate the robot from the initial location to a destination.

Natural language based indoor autonomous navigation

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for operation trajectory planning thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring three-dimensional sensing data of a material pile, to construct a three-dimensional model of the material pile based on the three-dimensional sensing data; determining a loading operation position of the engineering machinery equipment on the material pile based on the three-dimensional model of the material pile and structural design information of the engineering machinery equipment; and acquiring position information of a mechanical structural component of the engineering machinery equipment, and performing operation trajectory planning based on the position information of the mechanical structural component and the loading operation position, to generate an operation trajectory of the mechanical structural component executing a material loading operation.

Engineering machinery equipment, and method, system, and storage medium for operation trajectory planning thereof

Described herein are systems and methods for inverse reinforcement learning to leverage the benefits of model-based optimization method and model-free learning method. Embodiments of a framework combining human behavior model with model predictive control are presented. The framework takes advantage of feature identification capability of a neural network to determine the reward function of model predictive control. Furthermore, embodiments of the present approach are implemented to solve the practical autonomous driving longitudinal control problem with simultaneous preference on safe execution and passenger comfort.

Inverse reinforcement learning with model predictive control

Embodiments of the present disclosure disclose a method, apparatus, device, and storage medium for controlling a guide robot, and relate to the field of artificial intelligence, robots, and multi-sensor fusion technologies. A specific embodiment of the method includes: acquiring a state of the guide robot, a state of a user, and a position of an obstacle; generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user; generating a collision-free global path based on the position of the obstacle; generating a control command based on the state update equation for the combined system and the collision-free global path; and driving the guide robot to move based on the control command.

Method, apparatus, device, and storage medium for controlling guide robot

Described herein are embodiments of a neural network-based task planner (TaskNet) for autonomous vehicle. Given a high-level task, the TaskNet planner decomposes it into a sequence of sub-tasks, each of which is further decomposed into task primitives with specifications. TaskNet comprises a first model for predicating the global sequence of working area to cover large terrain, and a second model for determining local operation order and specifications for each operation. The neural models may include convolutional layers for extracting features from grid map-based environment representation, and fully connected layers to combine extracted features with past sequences and predict the next sub-task or task primitive. Embodiments of the TaskNet are trained using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator. Experiment results show TaskNet may effectively learn common task decomposition strategies and generate suitable sequences of sub-tasks and task primitives.

Neural task planner for autonomous vehicles

Embodiments of the present disclosure relate to a method and apparatus for updating information. The method may include: acquiring road network structure information of a target road network and vehicle information of a target number of vehicles in the target road network, the vehicle information including initial state information, perception information and positioning information, and the vehicle information being constrained by the road network structure information; selecting a target vehicle from the target number of vehicles; determining, based on a vehicle dynamics model, a reference speed at which the target vehicle passes a preset time step; and updating vehicle information of a vehicle in the target road network based on the reference speed of the target vehicle.

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Sunnyvale, CA, United States of America

Jinxin Zhao