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1.发明申请公开(公告)号：US20210341885A1
公开(公告)日：2021-11-04
申请号：US17191091
申请日：2021-03-03
申请人： Robert Bosch GmbH
摘要： A method of training a control strategy for a control. An exploration strategy for a current version of the control strategy is determined in each of several iterations. Several simulation runs are carried out, in each of which an action is selected in accordance with the exploration strategy, and it being checked if the selected action is safe, until a safe action has been selected or a maximum number of actions greater than or equal to two has been selected. A follow-up state of the state in the sequence of states is ascertained. The sequence of states are collected as data of the simulation run; for the iteration. The value of a loss function is ascertained over the data of the executed simulation runs and the control strategy is adapted so that the value of the loss function is reduced.
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公开(公告)号：US20220197227A1
公开(公告)日：2022-06-23
申请号：US17601366
申请日：2020-03-24
申请人： Robert Bosch GmbH
发明人： Jan Guenter Woehlke , Felix Schmitt
摘要： A computer-implemented method and device for activating a technical unit. The device includes an input for input data from at least one sensor, an output for activating the technical unit using an activation signal, and a computing device which activates the technical unit as a function of the input data. A state of at least one part of the technical unit or of surroundings is determined as a function of input data. At least one action is determined as a function of the state and of a strategy for the technical unit. Technical unit being activated to carry out the at least one action. The strategy, represented by an artificial neural network, is learned with a reinforcement learning algorithm in interaction with the technical unit or with the surroundings as a function of the at least one feedback signal. The feedback signal is determined as a function of a target-setting.
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公开(公告)号：US20210341904A1
公开(公告)日：2021-11-04
申请号：US17231786
申请日：2021-04-15
申请人： Robert Bosch GmbH
IPC分类号： G05B19/4155 , G06N7/00 , G06N3/08 , G05D1/02
摘要： A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.
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公开(公告)号：US11934176B2
公开(公告)日：2024-03-19
申请号：US17231786
申请日：2021-04-15
申请人： Robert Bosch GmbH
CPC分类号： G05B19/4155 , G05D1/0214 , G05D1/0221 , G06N3/08 , G06N7/01 , G05B2219/50391 , G05D1/0088
摘要： A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.
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公开(公告)号：US20240010236A1
公开(公告)日：2024-01-11
申请号：US18255849
申请日：2021-11-30
申请人： Robert Bosch GmbH
发明人： Felix Schmitt , Martin Stoll , Johannes Goth , Holger Andreas Banzhaf , Johannes Maximilian Doellinger , Michael Hanselmann
CPC分类号： B60W60/0015 , G06N7/01 , B60W2555/60 , B60W30/18163
摘要： A method for selecting a driving maneuver to be carried out by an at least semi-autonomously driving vehicle is disclosed. The method includes (i) using measurement data of at least one sensor carried by the vehicle, creating a representation of the situation the vehicle is in, (ii) mapping the representation of the situation to a probability distribution by way of a trained machine learning model, which probability distribution specifies a probability for every driving maneuver from a predefined catalog of available driving maneuvers, with which said driving maneuver is carried out, (iii) selecting a driving maneuver from the probability distribution as the driving maneuver to be carried out, (iv) in addition to using at least one aspect of the situation the vehicle is in, a subset of driving maneuvers which are disallowed in this situation is determined, and (v) this disallowed driving maneuver is prevented from being carried out.
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公开(公告)号：US20240346922A1
公开(公告)日：2024-10-17
申请号：US18631899
申请日：2024-04-10
申请人： Robert Bosch GmbH
CPC分类号： G08G1/0141 , B60W60/00276 , G08G1/0129 , G08G1/123
摘要： A method for evaluating a traffic scene with several road users includes (i) providing input data which results from recording of the traffic scene and which specifies the road users and associated features, the features being based at least in part on current and past states of the road users, (ii) providing a representation of the road users and their relationships to each other in the traffic scene and an infrastructure of the traffic scene, wherein the relationships are specified based on the features, wherein the infrastructure is represented by a parameterized representation, wherein the representation comprises a plurality of nodes of a graph representing the respective road users, and wherein the representation comprises a plurality of edges of the graph explicitly specifying the relationships of the road users to each other, (iii) predicting a future development of the traffic scene, wherein the prediction is performed taking into account the current and past states of the road users, wherein a behavior of all represented road users is predicted on the basis of the provided representation, and (iv) providing a result of the prediction.
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公开(公告)号：US20240202531A1
公开(公告)日：2024-06-20
申请号：US18508078
申请日：2023-11-13
申请人： Robert Bosch GmbH
发明人： Felix Schmitt
IPC分类号： G06N3/088 , G06N3/0455 , G06N3/0475
CPC分类号： G06N3/088 , G06N3/0455 , G06N3/0475
摘要： A system and method are provided for training an autoencoder on training data to obtain a generative model for synthesizing new data. During the training, an affine transformation is applied to the output of the encoder to obtain representations of the training data instances in the latent space. Furthermore, a mean and covariance of the representations of the training data instances in the latent space is determined, and parameters of the affine transformation are updated to shift the mean and covariance of the representations of the training data instances towards a target mean and a target covariance. The decoder of the trained autoencoder may be used as generative model, for example to synthesize input data to a test or simulation of a system, device, or machine, or to synthesize training data for the training of a(nother) machine learnable model.
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公开(公告)号：US20230128941A1
公开(公告)日：2023-04-27
申请号：US18045382
申请日：2022-10-10
申请人： Robert Bosch GmbH
发明人： Felix Schmitt
摘要： A method for controlling an agent. The method includes training a neural network using training data that contain, for a multiplicity of agents, examples of a behavior of the agents, the output of the neural network including a prediction of a behavior and being a function of network parameters that are trained in common for all training data, and being a function of a further parameter that is trained individually for each of the agents of the multiplicity of agents; fitting of a probability distribution to the values of the further parameter for the agents that result from the training; sampling a value from the probability distribution for a further agent in the environment of the agent; and controlling the agent, taking into account a prediction of the behavior of the further agent that the neural network outputs for the sampled value for the further agent.
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公开(公告)号：US20230090127A1
公开(公告)日：2023-03-23
申请号：US17898846
申请日：2022-08-30
申请人： Robert Bosch GmbH
IPC分类号： G05B13/02
摘要： A method for controlling an agent. The method includes obtaining numerical values of a first and second set of state variables, which together represent a current full state of the agent, and the numerical values of the first set of state variables represent a current partial state of the robot; determining a state value prior comprising, for potential subsequent partial states following the current partial state, an evaluation of the subsequent partial states in terms of achieving a goal to be attained by the agent; supplying an input comprising a local crop of the state value prior and the numerical values of the second set of state variables representing, together with the numerical values of the first set of state variables, the current full state to a neural network configured to output an evaluation of control actions and controlling the agent in accordance with control signals.
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