# US 12,558,779 B2 · Self-reconfigurable robot controller

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,558,779 B2
- Document ID: US12558779B2
- Status: Granted
- Kind: B2
- Application number: 18/193,797
- Filed: 2023-03-31
- Published: 2024-10-03
- Granted: 2026-02-24
- Earliest claimed: 2023-03-31
- Assignees: Intel Corporation
- Applicants: Intel Corporation
- Inventors: Alejandro Ibarra Von Borstel; Fernando Ambriz Meza; Cornelius Buerkle; Jose Rodrigo Camacho Perez; Hector Cordourier Maruri; Paulo Lopez Meyer; Fabian Oboril; Julio Zamora Esquivel; Jose Miguel Hernandez Miramontes
- Web page: https://patentgrep.com/patent/US12558779B2

## CPC

- B25J 9/163: {learning, adaptive, model based, rule based expert control}
- B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor}
- G05B 2219/39311: Multilayer, MNN, four layer perceptron, sigmoidal neural network
- B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50})
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)

## Abstract

A self-reconfigurable controller for a robot, including: input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and processing circuitry that is operable to: register the I/O peripheral devices and associated functionalities; receive a command for the robot to perform a task; conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices; and generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task.

## Claims

### Claim 1

A self-reconfigurable controller for a robot, comprising:
input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and
processing circuitry that is operable to:
register the I/O peripheral devices and associated functionalities, wherein the functionalities of the I/O peripheral devices are represented by functional semantic identifiers (FSIDs), which are non-random identifiers;
receive a command for the robot to perform a task;
conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices, wherein if the self-awareness check indicates a lack of a correlation of functionalities to perform the task with functionalities of the I/O peripheral devices, provide an indication of a missing functionality of the I/O peripheral devices; and
generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task, wherein the net of DL models comprise:
a topology transformer model operable to transform an input FSID and an output FSID of the functionalities to perform the task into a sequence representing a topology of the TDLC, wherein the sequence is divided into subsequences representing respective TDLC model layers;
a layer index classifier model operable to classify the subsequences with indexes; and
a parameter sequence generator model operable to predict parameters of the TDLC model layers based on the indexes,
wherein the TDLC model is generated layer-by-layer in a single iteration.

### Claim 2 (depends on 1)

The self-reconfigurable controller of claim 1, wherein the processing circuitry is operable to register each of the I/O peripheral devices by:
storing the FSID of the functionality of the I/O peripheral device in a functional semantic identifier table (FSIT), with FSIDs grouped by similarity of represented functionalities;
creating a corresponding FSID channel; and
storing the I/O peripheral device in a system status table (SST) as enabled.

### Claim 3 (depends on 2)

The self-reconfigurable controller of claim 2, wherein the SST comprises I/O peripheral device information received from the respective I/O peripheral devices, and the I/O peripheral device information comprises a universal unique identifier (UUID), a functional semantic identifier (FSID), and a compatible FSID.

### Claim 4 (depends on 1)

The self-reconfigurable controller of claim 1, wherein the processing circuitry is further operable to:
decode the command from a natural language format to an input FSID and an output FSID of the functionalities to perform the task,
wherein the correlation of the functionalities to perform the task with the functionalities of the I/O peripheral devices is performed based on the input FSID and the output FSID of the command with the FSIDs of the I/O peripheral devices.

### Claim 5 (depends on 1)

The self-reconfigurable controller of claim 1, wherein the processing circuitry is further operable to:
generate the TDLC model if the self-reconfigurable controller does not already have a suitable TDLC model.

### Claim 6

A component of a self-reconfigurable controller for a robot, wherein the self-reconfigurable controller has input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot, the component comprising:
processing circuitry; and
a non-transitory computer-readable storage medium including instructions that, when executed by the processing circuitry, cause the processing circuitry to:
register the I/O peripheral devices and associated functionalities, wherein the functionalities of the I/O peripheral devices are represented by functional semantic identifiers (FSIDs), which are non-random identifiers;
receive a command for the robot to perform a task;
conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices, wherein if a result of the self-awareness check is a lack of a correlation of functionalities to perform the task with functionalities of the I/O peripheral devices, provide an indication of a missing functionality of the I/O peripheral devices; and
generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task, wherein the net of DL models comprise:
a topology transformer model operable to transform an input FSID and an output FSID of the functionalities to perform the task into a sequence representing a topology of the TDLC, wherein the sequence is divided into subsequences representing respective TDLC model layers;
a layer index classifier model operable to classify the subsequences with indexes; and
a parameter sequence generator model operable to predict parameters of the TDLC model layers based on the indexes,
wherein the TDLC model is generated layer-by-layer in a single iteration.

### Claim 7 (depends on 6)

The component of claim 6, wherein the processing circuitry is operable to register each of the I/O peripheral devices by:
storing the FSID of the functionality of the I/O peripheral device in a functional semantic identifier table (FSIT), with FSIDs grouped by similarity of represented functionalities;
creating a corresponding FSID channel; and
storing the I/O peripheral device in a system status table (SST) as enabled.

### Claim 8 (depends on 7)

The component of claim 7, wherein the SST comprises I/O peripheral device information received from the respective I/O peripheral devices, and the I/O peripheral device information comprises a universal unique identifier (UUID), a functional semantic identifier (FSID), and a compatible FSID.

### Claim 9 (depends on 6)

The component of claim 6, wherein the processing circuitry is further operable to:
decode the command from a natural language format to an input FSID and an output FSID of the functionalities to perform the task,
wherein the correlation of the functionalities to perform the task with the functionalities of the I/O peripheral devices is performed based on the input FSID and the output FSID of the command with the FSIDs of the I/O peripheral devices.

### Claim 10 (depends on 6)

The component of claim 6, wherein the processing circuitry is further operable to:
generate the TDLC model if the component does not already have a suitable TDLC model.

### Claim 11

A robotic system, comprising:
input/output (I/O) peripheral devices coupled to a robot; and
a self-reconfigurable controller, comprising:
I/O interfaces to enable communication with the I/O peripheral devices; and
processing circuitry that is operable to:
register the I/O peripheral devices and associated functionalities, wherein the functionalities of the I/O peripheral devices are represented by functional semantic identifiers (FSIDs), which are non-random identifiers;
receive a command for the robot to perform a task;
conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices, wherein if a result of the self-awareness check is a lack of a correlation of functionalities to perform the task with functionalities of the I/O peripheral devices, provide an indication of a missing functionality of the I/O peripheral devices; and
generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task, wherein the net of DL models comprise:
a topology transformer model operable to transform an input FSID and an output FSID of the functionalities to perform the task into a sequence representing a topology of the TDLC, wherein the sequence is divided into subsequences representing respective TDLC model layers;
a layer index classifier model operable to classify the subsequences with indexes; and
a parameter sequence generator model operable to predict parameters of the TDLC model layers based on the indexes,
wherein the TDLC model is generated layer-by-layer in a single iteration.

### Claim 12 (depends on 11)

The robotic system of claim 11, wherein the processing circuitry is operable to register each of the I/O peripheral devices by:
storing the FSID of the functionality of the I/O peripheral device in a functional semantic identifier table (FSIT), with FSIDs grouped by similarity of represented functionalities;
creating a corresponding FSID channel; and
storing the I/O peripheral device in a system status table (SST) as enabled.