Intrinsic Innovation LLC

X Development LLC

Autofuss

Michael Beardsworth

Gregory Prisament

Marek Piotr Michalowski

Nicholas Julian Cox

Eli Reekmans

Sean Alexander Cassero

Nils Berg

Andre Gaschler

Benjamin Bremer

Kendra Byrne

Marcin Krzysztof Szczodrak

Jeffrey Linnell

Anthony Sean Jules

Jeff Linnell

Matthew A Bitterman

Bala Venkata Sai Ravi Krishna Kolluri

Brandon Kruysman

Jonathan Proto

Keegan Go

Ryan Butterfoss

Stoyan Gaydarov

Ytai Ben-Tsvi

Abhishek Shailesh Shah

Klas Jonas Alfred Kronander

Dmytro Hrybenko

Asa Kaplan

Karsten Knese

Ian Sherman

Gregory Lawrence

Steve Wolter

Sean Alexander Rowan

Marek Michalowski

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot to perform a custom real-time action. One of the methods comprises receiving, by a real-time robotics control framework, a definition of a custom real-time control function, wherein the definition specifies a plurality of actions and one or more custom reactions; repeatedly executing, by the real-time robotics control framework, the custom real-time control function at each tick of a real-time robotics system driving one or more physical robots, including: obtaining current values of one or more state variables, evaluating the one or more custom reactions specified by the custom real-time control function according to the current values of the one or more state variables, and whenever a custom reaction is satisfied, updating a current action in real time according to the custom reaction that is satisfied, and executing a next tick of the current action.

Real-time robotics control framework

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a real-time robotics control framework that implements a real-time clock with transferable ownership. One of the systems being configured to operate a real-time control loop for a robot in multiple modes include a base mode and an augmented mode, wherein when operating the robot in the base mode, the system is configured to operate a base real-time control loop in which a start of each tick of the base real-time control loop is initiated by a real-time software control module, and wherein when operating the robot in the augmented mode, the system is configured to operate multiple real-time control loops comprising the base real-time control loop and a robot real-time control loop in which a start of each tick of the real-time control loop is initiated by the real-time software drive module.

Transferable real-time clock for robotics control

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that distributes skill bundles that can guide robot execution. One of the methods includes receiving data for a skill bundle from a skill developer. The data can include a definition of one or more preconditions for a robotic system to execute a skill; one or more effects to an operating environment after the robotic system has executed the skill; and a software module implementing the skill. The software module can define a state machine of subtasks. A skill bundle can be generated from the data received from the skill developer. Data identifying the generated skill bundle can be added to a skill registry. The skill bundle can be provided to the execution robot system for installation on the robot execution system.

Robot instruction distribution framework

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using equipment-specific sample generators to automatically adapt a skill for execution in an operating environment. One of the methods includes obtaining a skill to be executed in an operating environment having one or more robots, wherein the skill defines a sequence of subtasks to be performed by the one or more robots in the working environment. A planning process is performed to generate a motion plan for the one or more robots, including obtaining, from an equipment-specific sample generator, a plurality of equipment-specific samples for a subtask of the skill, generating, from the plurality of equipment-specific samples, a plurality of candidate motion plans, and selecting, from the plurality of candidate motion plans, a motion plan for the one or more robots to perform the skill in the operating environment.

Equipment specific motion plan generation for robotic skill adaptation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot to perform a custom real-time action that uses a callback function. One of the methods comprises receiving a definition of a custom real-time control function that specifies a custom callback function, an action, and a custom reaction that references the custom callback function; providing a command to initiate the action; repeatedly executing, by the control layer of the real-time robotics control framework, the custom real-time control function at each tick of a real-time robotics system driving one or more physical robots, including: obtaining current values of one or more state variables, evaluating the custom reaction specified by the custom real-time control function according to the current values of the one or more state variables, and whenever the one or more conditions of the custom reaction are satisfied, invoking the custom callback function.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot to perform a custom real-time action that uses streaming inputs. One of the methods comprises receiving a definition of a custom real-time streaming control function that defines a custom streaming action, wherein the custom streaming action specifies a goal state for a robot in an operating environment; providing a command to initiate the custom streaming action; and repeatedly providing updated goal states for the custom streaming action, wherein the control layer of the framework is configured to execute the custom streaming action including driving the robot toward a most recent goal state at each tick of a real-time robotics control cycle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing transformation mode switching in a robotics control system. One of the methods includes receiving data representing a state machine that defines one or more portions of a robotics task; executing a first control loop corresponding to a first node of the state machine, wherein executing the first control loop comprises providing commands to the robotic components computed from a first coordinate transformation process; determining, based on one or more status messages, that an exit condition for the first node has been satisfied; performing a mode switch between the first coordinate transformation process and a different second coordinate transformation process; and executing a second control loop corresponding to a second node of the state machine, wherein executing the second control loop comprises providing commands to the robotic components computed from the second coordinate transformation process.

Transformation mode switching for a real-time robotic control system

This specification describes systems, methods, devices, and other techniques for planning workspaces for automated fabrication processes. A computing system facilitates planning by receiving a set of parameters for planning a layout of a workspace for an automated fabrication process, and generating a plurality of candidate workspace layouts, including selecting, for each candidate workspace layout, (i) one or more robots for performing tasks in the automated fabrication process and (ii) corresponding locations for the one or more robots within the workspace. The system determines an optimal workspace layout based on the plurality of candidate workspace layouts, generates a workspace layout specification for the optimal workspace layout, and provides the workspace layout specification to one or more second computing systems.

Robotic workspace layout planning

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing interpolated robot control parameters. One of the methods includes receiving, by a real-time bridge from a control agent for a robot, a non-real-time command for the robot, wherein the non-real-time command specifies a trajectory to be attained by a component of the robot and a target value for a control parameter, wherein the control parameter controls how a real-time controller will cause the robot to react to one or more external stimuli encountered during a control cycle of the real-time controller. The real-time bridge provides the one or more real-time commands translated from the non-real-time command and interpolated control parameter information to the real-time controller, thereby causing the robot to effectuate the trajectory of the non-real-time command according to the interpolated control parameter information.

Robot control parameter interpolation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing real-time code analysis. One of the methods includes receiving a request to perform real-time code analysis on source code, wherein the request identifies one or more target source code elements. A plurality of real-time annotation values occurring in the source code for the target source code elements are identified. A call graph is generated for the one or more target source code elements. The call graph is traversed to identify one or more real-time violations, wherein each real-time violation is an instance in the source code that violates one or more real-time safe criteria. An output is provided that identifies one or more of the real-time violations occurring in the source code.

Analysis of real-time software

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing automated safety procedures for a robot. One of the methods includes receiving, by a robotic control system for a robot, a request to execute an automated safety procedure by a safety control subsystem for the robot. Each step of the automated safety procedure is iterated until an end of the automated safety procedure is reached, including if a step requires a new safety configuration, a respective safety configuration for the step is obtained and activated before performing one or more automatic actions for the step.

Automated safety procedures for human intervention in robot systems

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for logging real-time data of a robot control system. One of the systems comprises a real-time robotic control system comprising one or more computers, programmed to perform operations comprising i) software module loops and ii) real-time data logging, wherein each software module loop comprises a plurality of software modules executed repeatedly in a predetermined sequence, each software module executes within a predetermined time window, and the real-time data logging comprises copying segments of real-time data used by a particular software module to a buffer accessible by a non-real-time downstream system, wherein each segment of real-time data is copied at a respective predetermined time relative to execution of the software modules in the software module loop; and the non-real-time downstream system comprising one or more computers, the non-real-time downstream system programmed to perform operations that consume the real-time messages.

Data logger for a real-time robotic control system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a composability framework that supports the coordination of the low-level actions of multiple subsystems. One of the methods comprises receiving a multi-device application definition for a real-time robotic control system configured to perform operations comprising controlling a robotic execution environment having multiple robotic components, wherein the application definition comprises: one or more custom high-level software modules for each robotic component of the multiple robotic components; respective module interface definitions for each of the custom high-level software modules, wherein a module interface definition of a custom high-level software module specifies one or more asynchronous or real-time communication channels; and respective device interface definitions for each of the multiple robotic components; and processing the application definition to generate output programs to be executed respectively by the multiple robotic components.

Composability framework for robotic control system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for deblurring an image captured by a robot-mounted camera. One of the methods comprises capturing, using a camera that is attached to an arm of a robot, an image at a first time, wherein the image exhibits motion blur, and wherein the exhibited blur was caused by movement of the arm of the robot at the first time; receiving, from a robot control system of the robot, motion data characterizing the movement of the arm of the robot at the first time; generating a motion kernel using the received motion data; and generating a deblurred image by processing the image using the motion kernel.

Reducing motion blur for robot-mounted cameras

Disclosed herein is a worksite automation process that involves: generating a first sequence of tasks to build the product according to a model. The process further involves causing one or more robotic devices to build the product by beginning to execute the first sequence of tasks. Further, during the execution of the first sequence of tasks, performing a buildability analysis to determine a feasibility of completing the product by executing the first sequence of tasks. Based on the analysis, determining that it is not feasible to complete the product by executing the first sequence of tasks, and in response, generating a second sequence of tasks to complete the product according to the model. Then, causing the one or more robotic devices to continue building the product by beginning to execute the second sequence of tasks.

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San Francisco, CA, United States of America

Michael Beardsworth