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公开(公告)号：US20180345496A1
公开(公告)日：2018-12-06
申请号：US15995005
申请日：2018-05-31
Applicant: AUTODESK, INC.
Inventor： Hui LI , Evan Patrick ATHERTON , Erin BRADNER , Nicholas COTE , Heather KERRICK
IPC: B25J9/16
Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.
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公开(公告)号：US20250091201A1
公开(公告)日：2025-03-20
申请号：US18443209
申请日：2024-02-15
Applicant: AUTODESK, INC.
Inventor： Hui LI , Xiang ZHANG
IPC: B25J9/16
Abstract: One embodiment of a method for controlling a robot includes generating, via a first trained machine learning model, a robot motion and a predicted force associated with the robot motion, determining, via a second trained machine learning model, a gain associated with the predicted force, generating one or more robot commands based on the robot motion and the gain, and causing a robot to move based on the one or more robot commands.
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公开(公告)号：US20240255917A1
公开(公告)日：2024-08-01
申请号：US18629686
申请日：2024-04-08
Applicant: AUTODESK, INC.
Inventor： Evan Patrick ATHERTON , David THOMASSON , Maurice Ugo CONTI , Heather KERRICK , Nicholas COTE , Hui LI
IPC: G05B19/4099 , B22D23/00 , B23K9/04 , B33Y50/00
CPC classification number: G05B19/4099 , B22D23/003 , B33Y50/00 , B23K9/044 , G05B2219/49023 , G06T2219/008
Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.
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公开(公告)号：US20190084158A1
公开(公告)日：2019-03-21
申请号：US15709361
申请日：2017-09-19
Applicant: AUTODESK, INC.
Inventor： Evan ATHERTON , David THOMASSON , Heather KERRICK , Hui LI
IPC: B25J9/16 , G05B19/048 , G05B19/418
Abstract: A robot system models the behavior of a user when the user occupies an operating zone associated with a robot. The robot system predicts future behaviors of the user, and then determines whether those predicted behaviors interfere with anticipated behaviors of the robot. When such interference may occur, the robot system generates dynamics adjustments that can be implemented by the robot to avoid such interference. The robot system may also generate dynamics adjustments that can be implemented by the user to avoid such interference.
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公开(公告)号：US20190076949A1
公开(公告)日：2019-03-14
申请号：US15702637
申请日：2017-09-12
Applicant: AUTODESK, INC.
Inventor： Evan ATHERTON , David THOMASSON , Heather KERRICK , Hui LI
Abstract: A control application implements computer vision techniques to cause a positioning robot and a welding robot to perform fabrication operations. The control application causes the positioning robot to place elements of a structure at certain positions based on real-time visual feedback captured by the positioning robot. The control application also causes the welding robot to weld those elements into place based on real-time visual feedback captured by the welding robot. By analyzing the real-time visual feedback captured by both robots, the control application adjusts the positioning and welding operations in real time.
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Patent Agency Ranking
公开(公告)号：US20220105626A1
公开(公告)日：2022-04-07
申请号：US17471520
申请日：2021-09-10
Applicant: AUTODESK, INC.
Inventor： Jieliang LUO , Hui LI
Abstract: Techniques are disclosed for training and applying machine learning models to control robotic assembly. In some embodiments, force and torque measurements are input into a machine learning model that includes a memory layer that introduces recurrency. The machine learning model is trained, via reinforcement learning in a robot-agnostic environment, to generate actions for achieving an assembly task given the force and torque measurements. During training, experiences are collected as transitions within episodes, the transitions are grouped into sequences, and the last two sequences of each episode have a variable overlap. The collected transitions are stored in a prioritized sequence replay buffer, from which a learner samples sequences to learn from based on transition and sequence priorities. Once trained, the machine learning model can be deployed to control various types of robots to perform the assembly task based on force and torque measurements acquired by sensors of those robots.
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公开(公告)号：US20180348735A1
公开(公告)日：2018-12-06
申请号：US15613070
申请日：2017-06-02
Applicant: AUTODESK, INC.
Inventor： Evan Patrick ATHERTON , David THOMASSON , Maurice Ugo CONTI , Heather KERRICK , Nicholas COTE , Hui LI
IPC: G05B19/4099 , B22D23/00 , B33Y50/00 , B23K9/04 , B23K9/095
CPC classification number: G05B19/4099 , B22D23/003 , B23K9/044 , B33Y50/00 , G05B2219/49023
Abstract: An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.
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公开(公告)号：US20180349527A1
公开(公告)日：2018-12-06
申请号：US15995003
申请日：2018-05-31
Applicant: AUTODESK, INC.
Inventor： Hui LI , Evan Patrick ATHERTON , Erin BRADNER , Nicholas COTE , Heather KERRICK
Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.
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公开(公告)号：US20220193912A1
公开(公告)日：2022-06-23
申请号：US17691838
申请日：2022-03-10
Applicant: AUTODESK, INC.
Inventor： Hui LI , Evan Patrick ATHERTON , Erin BRADNER , Nicholas COTE , Heather KERRICK
Abstract: One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.
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公开(公告)号：US20200147794A1
公开(公告)日：2020-05-14
申请号：US16667843
申请日：2019-10-29
Applicant: AUTODESK, INC.
Inventor： Heather KERRICK , Erin BRADNER , Hui LI , Evan Patrick ATHERTON , Nicholas COTE
IPC: B25J9/16 , G05B19/4097
Abstract: An assembly engine is configured to generate, based on a computer-aided design (CAD) assembly, a set of motion commands that causes the robot to manufacture a physical assembly corresponding to the CAD assembly. The assembly engine analyzes the CAD assembly to determine an assembly sequence for various physical components to be included in the physical assembly. The assembly sequence indicates the order in which each physical component should be incorporated into the physical assembly and how those physical components should be physically coupled together. The assembly engine further analyzes the CAD assembly to determine different component paths that each physical component should follow when being incorporated into the physical assembly. Based on the assembly sequence and the component paths, the assembly engine generates a set of motion commands that the robot executes to assemble the physical components into the physical assembly.
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