# US 2026/0097490 A1 · Deployment for unified neural motion control in robotics systems and applications

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- Number: US 2026/0097490 A1
- Document ID: US20260097490A1
- Status: Published application
- Kind: A1
- Application number: 19/080,738
- Filed: 2025-03-14
- Published: 2026-04-09
- Earliest claimed: 2024-10-07
- Assignees: NVIDIA Corporation
- Applicants: NVIDIA Corporation
- Inventors: Tairan HE; Wenli XIAO; Toru LIN; Zhengyi LUO; Zhenjia XU; Zhenyu JIANG; Jan KAUTZ; Xiaolong WANG; Linxi FAN; Yuke ZHU
- Web page: https://patentgrep.com/patent/US20260097490A1

## CPC

- B25J 9/163: {learning, adaptive, model based, rule based expert control}
- B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor}
- B25J 9/1661: {characterised by task planning, object-oriented languages}
- 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})

## Abstract

In various examples, a technique for neural motion control includes determining a plurality of masks associated with one or more control modes for an articulated object. The technique also includes applying the plurality of masks to a plurality of goal state attributes for the articulated object to produce one or more masked goal state attributes. The technique further includes generating, via execution of a trained machine learning model, one or more actions based at least on the one or more masked goal state attributes and performing a task using the articulated object based at least on the one or more actions.

## Claims

### Claim 1

A method comprising:
determining a plurality of masks associated with one or more control modes for an articulated object;
applying the plurality of masks to a plurality of goal state attributes for the articulated object to produce one or more masked goal state attributes;
generating, via execution of a trained machine learning model, one or more actions based at least on the one or more masked goal state attributes; and
performing a task using the articulated object based at least on the one or more actions.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
generating, via execution of a machine learning model, one or more additional actions based at least on one or more additional goal state attributes; and
updating one or more parameters of the machine learning model based at least on the one or more additional actions and one or more reference actions to produce the trained machine learning model.

### Claim 3 (depends on 2)

The method of claim 2, further comprising generating, via execution of a second trained machine learning model, the one or more reference actions based at least on the plurality of goal state attributes.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
determining the plurality of masks based at least on a selection of the one or more control modes; and
determining the plurality of goal state attributes based at least on a control input received via a control interface.

### Claim 5 (depends on 1)

The method of claim 1, wherein the plurality of masks is used to filter a subset of the plurality of goal state attributes that is not associated with the one or more control modes.

### Claim 6 (depends on 1)

The method of claim 1, wherein the one or more control modes comprise a first control mode for a first portion of the articulated object and a second control mode for a second portion of the articulated object.

### Claim 7 (depends on 1)

The method of claim 1, wherein the task comprises at least one of bimanual manipulation, bipedal locomotion, or navigation.

### Claim 8 (depends on 1)

The method of claim 1, wherein the plurality of goal state attributes comprises at least one of a set of joint positions, a set of joint angles, or a set of root attributes.

### Claim 9 (depends on 1)

The method of claim 1, wherein the plurality of goal state attributes is associated with at least one of a kinematic position tracking command space, a joint angle tracking command space, or a root tracking command space.

### Claim 10 (depends on 1)

The method of claim 1, wherein the articulated object comprises a humanoid robot.

### Claim 11

At least one processor comprising:
processing circuitry to perform operations comprising:
applying a plurality of masks associated with one or more control modes for an articulated object to a plurality of goal state attributes associated with a plurality of command spaces for the articulated object to produce one or more masked goal state attributes;
generating, via execution of a trained machine learning model, one or more actions based at least on the one or more masked goal state attributes and a proprioception associated with the articulated object; and
performing a task using the articulated object based at least on the one or more actions.

### Claim 12 (depends on 11)

The at least one processor of claim 11, wherein the operations further comprise:
generating, via execution of a machine learning model, one or more additional actions based at least on one or more additional goal state attributes associated with the articulated object; and
updating one or more parameters of the machine learning model based at least on the one or more additional actions and one or more reference actions for the articulated object to produce a trained machine learning model.

### Claim 13 (depends on 12)

The at least one processor of claim 12, wherein the operations further comprise:
updating one or more additional parameters of a second machine learning model based at least on a set of reference motions and a set of rewards to produce a second trained machine learning model; and
generating, via execution of the second trained machine learning model based at least on an additional proprioception and the one or more additional goal state attributes, the one or more reference actions.

### Claim 14 (depends on 13)

The at least one processor of claim 13, wherein the set of rewards is computed based at least on at least one of a penalty term, a regularization term, or a set of task rewards.

### Claim 15 (depends on 12)

The at least one processor of claim 12, wherein the operations further comprise determining the one or more additional goal state attributes based on at least one of:
a first set of masks associated with one or more selected command spaces included in the plurality of command spaces; and
a second set of masks applied to a subset of the plurality of goal state attributes that is associated with the one or more selected command spaces.

### Claim 16 (depends on 11)

The at least one processor of claim 11, wherein the operations further comprise determining the plurality of masks and the plurality of goal state attributes based at least on a control input and the one or more control modes.

### Claim 17 (depends on 11)

The at least one processor of claim 11, wherein the proprioception comprises at least one of a joint position, a joint velocity, a base angular velocity, a gravity vector, or an action history.

### Claim 18 (depends on 11)

The at least one processor of claim 11, wherein the at least one processor is comprised in at least one of:
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system implemented using a robot;
a system for performing one or more conversational AI operations;
a system implemented using one or more large language models (LLMs);
a system implemented using one or more small language models (SLMs);
a system implementing one or more vision language models (VLMs);
a system implementing one or more multi modal language models;
a system for generating synthetic data;
a system for performing one or more generative AI operations;
a system using or deploying one or more inference microservices;
a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.

### Claim 19

A system comprising:
one or more processors to generate a motion on an articulated object based at least on one or more actions generated via execution of a trained machine learning model, wherein the trained machine learning model is generated by training a machine learning model using a plurality of masked goal state attributes, and wherein the plurality of masked goal state attributes are generated by applying one or more masks to a plurality of goal state attributes associated with a plurality of command spaces for the articulated object.

### Claim 20 (depends on 19)

The system of claim 19, wherein the system is comprised in at least one of:
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system implemented using a robot;
a system for performing one or more conversational AI operations;
a system implemented using one or more large language models (LLMs);
a system implemented using one or more small language models (SLMs);
a system implementing one or more vision language models (VLMs);
a system implementing one or more multi modal language models;
a system for generating synthetic data;
a system for performing one or more generative AI operations;
a system incorporating one or more virtual machines (VMs);
a system using or deploying one or more inference microservices;
a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.