Autodesk, Inc.

Erin Marie Bradner

Evan Patrick Atherton

Hui Li

Heather Kerrick

Nicholas Cote

Mark Thomas Davis

George William Fitzmaurice

Tovi Samuel Grossman

Justin Frank Matejka

Francesco Iorio

Michael Glueck

Michael Bergin

Karl Willis

Michael Haley

Benjamin David Cochran

Negar Arabani

Ali Baradaran Hashemi

James La Fleur

Arthur Harsuvanakit

Heike Rapp-Wurm

David Thornton

Pantelis Katsiaris

Ken Hung

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.

Techniques for CAD-informed robotic assembly

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.

Adapting simulation data to real-world conditions encountered by physical processes

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.

A model generator implements a data-driven approach to generating a robot model that describes one or more physical properties of a robot. The model generator generates a set of basis functions that generically describes a range of physical properties of a wide range of systems. The model generator then generates a set of coefficients corresponding to the set of basis functions based on one or more commands issued to the robot, one or more corresponding end effector positions implemented by the robot, and a sparsity constraint. The model generator generates the robot model by combining the set of basis functions with the set of coefficients. In doing so, the model generator disables specific basis functions that do not describe physical properties associated with the robot. The robot model can subsequently be used within a robot controller to generate commands for controlling the robot.

Techniques for generating controllers for robots

A design application is configured to visualize and explore large-scale generative design datasets. The design explorer includes a graphical user interface (GUI) engine that generates a design explorer, a composite explorer, and a tradeoff explorer. The design explorer displays a visualization of a multitude of design options included in a design space. The design explorer allows a user to filter the design space based on input parameters that influence a generative design process as well as various design characteristics associated with the different design options. The composite explorer displays a fully interactive composite of multiple different design options. The composite explorer exposes various tools that allow the user to filter the design space via interactions with the composite. The tradeoff explorer displays a tradeoff space based on different rankings of design options. The different rankings potentially correspond to competing design characteristics specified by different designers.

Techniques for visualizing and exploring large-scale generative design datasets

A design application allows an end-user to define an engineering problem, and then synthesizes a spectrum of design options that solve the engineering problem. The design application then generates various tools to allow the end-user to explore that spectrum of design options. The design application allows the end-user to compare various attributes of each design option, and to filter the spectrum of design options based on those attributes. In response to end-user selections of certain design options, the design application identifies other similar design options, and then displays these design options to the end-user.

Approach for generating and exploring a design space

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconfigurable spaces. One of the methods includes identifying plan information relating to a space under evaluation. Constraints related to structures associated with the plan information are identified. Input regarding uses or elements to be included in a reconfigurable design for the space is received. A library of elements for inclusion in the space is evaluated, including determining one or more reconfigurable elements that satisfy the received input. A first configuration of a reconfigurable element is determined including a first placement in a first design associated with the space, and the first design in accordance with the first configuration is presented. A second different configuration is determined, including a second placement in a second different design associated with the space, and the second different design in accordance with the second different configuration is presented.

Reconfigurable spaces

Methods, systems, and apparatus, including computer programs encoded on a storage medium, for integrating components into objects that are to be 3D printed. One of the methods includes obtaining a three-dimensional model of a first object; receiving an input identifying an additional component; generating a digital representation of the additional component; integrating the digital representation of the additional component with the three-dimensional model; and providing the three-dimensional model with the integrated digital representation to a 3D printer for fabrication.

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Berkeley, CA, United States of America

Erin Marie Bradner