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公开(公告)号：US11667034B2
公开(公告)日：2023-06-06
申请号：US16788840
申请日：2020-02-12
CPC分类号： B25J9/1664 , B25J9/162 , B25J9/1669 , B25J9/1697 , G05B2219/39484
摘要： Computerized system and method are provided. A robotic manipulator (12) is arranged to grasp objects (20). A gripper (16) is attached to robotic manipulator (12), which includes an imaging sensor (14). During motion of robotic manipulator (12), imaging sensor (14) is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor (18) is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor (18) is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor (18) is configured to select a grasp location likely to result in a successful grasp of the respective object.
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2.发明申请公开(公告)号：US20220067526A1
公开(公告)日：2022-03-03
申请号：US17416017
申请日：2019-01-14
摘要： A computer-implemented method for training a neural network on a hardware accelerator of an edge device includes dividing a trained neural network into a domain independent portion and a domain dependent portion. The domain independent portion of the neural network is deployed onto a dedicated neural network processing unit of the hardware accelerator of the edge device, and the domain dependent portion of the neural network is deployed onto one or more additional processors of the hardware accelerator of the edge device. The domain dependent portion on the additional processors of the hardware accelerator is retrained using data collected at the edge device.
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公开(公告)号：US20210247744A1
公开(公告)日：2021-08-12
申请号：US17253146
申请日：2018-08-09
IPC分类号： G05B19/418
摘要： For manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32). The reinforcement is constrained (22) to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.
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4.发明申请公开(公告)号：US20190091859A1
公开(公告)日：2019-03-28
申请号：US16119191
申请日：2018-08-31
摘要： Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.
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公开(公告)号：US20240335941A1
公开(公告)日：2024-10-10
申请号：US18290831
申请日：2021-08-31
发明人： Juan L. Aparicio Ojea , Heiko Claussen , Ines Ugalde Diaz , Martin Sehr , Eugen Solowjow , Chengtao Wen , Wei Xi Xia , Xiaowen Yu , Shashank Tamaskar
IPC分类号： B25J9/16
CPC分类号： B25J9/1661 , B25J9/1664 , B25J9/1697
摘要： It is recognized herein that current approaches to autonomous operations are often limited to grasping and manipulation operations that can be performed in a single step. It is further recognized herein that there are various operations in robotics (e.g., assembly tasks) that require multiple steps or a sequence of motions to be performed. To determine or plan a sequence of motions for fulfilling a task, an autonomous system that includes a robot can perform object recognition, pose estimation, affordance analysis, decision-making, probabilistic task or motion planning, and object manipulation.
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公开(公告)号：US11914350B2
公开(公告)日：2024-02-27
申请号：US17253146
申请日：2018-08-09
IPC分类号： G05B19/418
CPC分类号： G05B19/4188 , G05B2219/32335 , G05B2219/40499
摘要： For manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32). The reinforcement is constrained (22) to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.
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公开(公告)号：US20230158679A1
公开(公告)日：2023-05-25
申请号：US17995313
申请日：2020-04-06
发明人： Chengtao Wen , Heiko Claussen , Xiaowen Yu , Eugen Solowjow , Richard Gary McDaniel , Swen Elpelt , Juan L. Aparicio Ojea
CPC分类号： B25J9/1697 , B25J9/161 , G06T17/00
摘要： Autonomous operations, such as robotic grasping and manipulation, in unknown or dynamic environments present various technical challenges. For example, three-dimensional (3D) reconstruction of a given object often focuses on the geometry of the object without considering how the 3D model of the object is used in solving or performing a robot operation task. As described herein, in accordance with various embodiments, models are generated of objects and/or physical environments based on tasks that autonomous machines perform with the objects or within the physical environments. Thus, in some cases, a given object or environment may be modeled differently depending on the task that is performed using the model. Further, portions of an object or environment may be modeled with varying resolutions depending on the task associated with the model.
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公开(公告)号：US20220391565A1
公开(公告)日：2022-12-08
申请号：US17824300
申请日：2022-05-25
发明人： Chengtao Wen , Juan L. Aparicio Ojea , Ines Ugalde Diaz , Gokul Narayanan Sathya Narayanan , Eugen Solowjow , Wei Xi Xia , Yash Shahapurkar , Shashank Tamaskar , Heiko Claussen
IPC分类号： G06F30/27
摘要： A method for automatically generating a bill of process in a manufacturing system comprising: receiving design information representative of a product to be produced; iteratively performing simulations of the manufacturing system; identifying manufacturing actions based on the simulations; optimizing the identified manufacturing actions to efficiently produce the product to be produced; generating, by the manufacturing system, a bill of process for producing the product. Simulations may be performed using a digital twin of the product being produced and a digital twin of the environment. System actions are optimized using a reinforcement learning technique to automatically produce a bill of process based on the design information of the product and task specifications.
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9.发明申请公开(公告)号：US20220297295A1
公开(公告)日：2022-09-22
申请号：US17666836
申请日：2022-02-08
发明人： Juan L. Aparicio Ojea , Heiko Claussen , Ines Ugalde Diaz , Yash Shahapurkar , Eugen Solowjow , Chengtao Wen , Wei Xi Xia , Gokul Narayanan Sathya Narayanan , Shashank Tamaskar
摘要： A computer-implemented method for designing execution of a process by a robotic cell includes obtaining a process goal and one or more process constraints. The method includes accessing a library of constructs and a library of skills. Each construct includes a digital representation of a component of the robotic cell or a geometric transformation of the robotic cell. Each skill includes a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective. The method uses a simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal, and determine a set of feasible designs that meet the one or more process constraints. The method includes outputting recommended designs from the set of feasible designs.
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10.发明申请公开(公告)号：US20200254609A1
公开(公告)日：2020-08-13
申请号：US16788904
申请日：2020-02-12
摘要： A system includes a robot device that comprises a non-transitory computer readable medium and a robot controller. The non-transitory computer readable medium stores one or more machine-specific modules comprising base neural network layers. The robot controller receives a task-specific module comprising information corresponding to one or more task-specific neural network layers enabling performance of a task. The robot controller collects one or more values from an operating environment, and uses the values as input to a neural network comprising the base neural network layers and the task-specific neural network layers to generate an output value. The robot controller may then perform the task based on the output value.
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