Huawei Technologies Co., Limited

Daniel Mark Graves

Kasra Rezaee

Jun Luo

Borislav Mavrin

Jun Jin

A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.

Method and system for adaptively controlling object spacing

Methods and systems for predictive control of an autonomous vehicle are described. Predictions of lane centeredness and road angle are generated based on data collected by sensors on the autonomous vehicle and are combined to determine a state of the vehicle that are then used to generate vehicle actions for steering control and speed control of the autonomous vehicle.

Method and system for predictive control of vehicle using digital images

Methods and systems are described for support policy learning in an agent of a robot. A general value function (GVF) is learned for a main policy, where the GVF represents future performance of the agent executing the main policy for a given state of the environment. A master policy selects an action based on the predicted accumulated success value received from the general value function. When the predicted accumulated success value is an acceptable value, the action selected by the master policy is execution of the main policy. When the predicted accumulated success value is not an acceptable value, the master action causes a support policy to be learned. The support policy generates a support action to be performed which causes the robot to transition from to a new state where the predicted accumulated success value has an acceptable value.

Methods and systems for support policy learning

A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.

Systems and methods for learning reusable options to transfer knowledge between tasks

A method or system for controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle, comprising: receiving data representative of at least one social object and determining a current state of the ego vehicle based on sensor data; predicting an ego safety value corresponding to the ego vehicle, for each possible behavior action in a set of possible behavior actions, based on the current state; predicting a social safety value corresponding to the at least one social object in the environment of the ego vehicle, based on the current state, for each possible behavior action; and selecting a next behavior action for the ego vehicle, based on the ego safety values, the social safety values, and one or more target objectives for the ego vehicle.

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Edmonton, Canada

Daniel Mark Graves