IP Library › Granted Patent US 12,358,138
Granted Patent B2
US 12,358,138 · App. 18/056,443 · Granted Jul 15, 2025

Machine learning logic-based adjustment techniques for robots

Inventors: Alexander Lonsberry (Gahanna, OH); Andrew Lonsberry (Columbus, OH); Nima Ajam Gard (Columbus, OH); Madhavun Candadai Vasu (Columbus, OH); Eric Schwenker (Columbus, OH)
B25J9/1664B25J9/161B25J9/163B25J11/005B25J19/021
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Quick Facts
Patent No.
US 12,358,138
App. No.
18/056,443
Granted
Jul 15, 2025
Kind
B2
Abstract

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, that provide for training, implementing, or updated machine learning logic, such as an artificial neural network, to model a manufacturing process performed in a manufacturing robot environment. For example, the machine learning logic may be trained and implemented to learn from or make adjustments based on one or more operational characteristics associated with the manufacturing robot environment. As another example, the machine learning logic, such as a trained neural network, may be implemented in a semi-autonomous or autonomous manufacturing robot environment to model a manufacturing process and to generate a manufacturing result. As another example, the machine learning logic, such as the trained neural network, may be updated based on data that is captured and associated with a manufacturing result. Other aspects and features are also claimed and described.

Claims (30)

1. A computer-implemented method of operating a welding robot, the computer-implemented method comprising:

simulating, using machine learning logic, a difference profile generated based on a comparison of a first weld profile and a reference weld profile, wherein the first weld profile comprises a geometric representation of a shape of a first weld, wherein the reference weld profile comprises a geometric representation of a shape of a reference weld;

determining one or more derivatives of the difference profile with respect to one or more weld parameters;

generating one or more updated weld parameters based upon the one or more derivatives;

transmitting, to the welding robot, control information to instruct the welding robot to perform a weld operation on a seam to form a second weld, wherein the control information indicates the one or more updated weld parameters; and

performing, by the welding robot, the weld operation on the seam using the control information.

2. The computer-implemented method of claim 1 , wherein the difference profile comprises differences in coordinates or images of the first weld profile and the reference weld profile.

3. The computer-implemented method of claim 1 , wherein at least one of the first weld profile or the reference weld profile is simulated using the machine learning logic.

4. The computer-implemented method of claim 1 , wherein the reference weld is based on sensor data received from one or more sensors.

5. The computer-implemented method of claim 1 , wherein the geometric representation of the shape of the first weld comprises point cloud information.

6. The computer-implemented method of claim 5 , wherein the geometric representation of the shape of the first weld comprises point cloud information of a slice of the first weld.

7. The computer-implemented method of claim 1 , further comprising:

determining a second weld profile based on sensor data received from one or more sensors, the sensor data associated with the second weld formed by the welding robot based on the control information, wherein the second weld profile comprises a geometric representation of a shape of the second weld; and

providing the machine learning logic with at least the second weld profile.

8. The computer-implemented method of claim 7 , wherein providing the machine learning logic with at least the second weld profile comprises updating the machine learning logic.

9. The computer-implemented method of claim 1 , wherein the machine learning logic is trained using shapes of weld beads.

10. The computer-implemented method of claim 1 , wherein the machine learning logic comprises one or more neural networks.

11. A computer-implemented method for operating a welding robot, the computer-implemented method comprising:

determining a first weld profile of a first weld formed by the welding robot based on first sensor data received from one or more sensors, wherein the first weld profile comprises a geometric representation of a shape of the first weld;

generating, using machine learning logic, a first difference profile based on the first weld profile and a reference weld profile comprising a geometric representation of a shape of a reference weld, and at least one updated welding parameter based on the first difference profile;

transmitting, to the welding robot, control information to instruct the welding robot to perform a weld operation to form a second weld, wherein the control information indicates the at least one updated welding parameter;

performing, by the welding robot, the weld operation using the control information;

determining a second weld profile of the second weld based on second sensor data received from the one or more sensors, wherein the second weld profile comprises a geometric representation of a shape of the second weld; and

generating, using the machine learning logic, a second difference profile based on the second weld profile and the reference weld profile.

12. The computer-implemented method of claim 11 , wherein the first weld profile comprises point cloud information.

13. The computer-implemented method of claim 12 , wherein the first weld profile comprises point cloud information of a slice of the first weld.

14. The computer-implemented method of claim 11 , wherein the reference weld profile is simulated by the machine learning logic.

15. The computer-implemented method of claim 11 , wherein the first difference profile comprises differences in coordinates or images of the first weld profile and the reference weld profile.

16. The computer-implemented method of claim 11 , wherein providing the machine learning logic based with at least one of the first difference profile or the second difference profile comprises updating the machine learning logic based upon the at least one of the first difference profile or the second difference profile.

17. The computer-implemented method of claim 11 , wherein the machine learning logic comprises one or more neural networks.

Assignments (5)
RELEASE OF SECURITY INTEREST Recorded May 19, 2026
From: TRIPLEPOINT PRIVATE VENTURE CREDIT INC.
To: PATH ROBOTICS, INC.
Reel/Frame 074700/0957 →
SECURITY INTEREST Recorded Apr 29, 2026
From: PATH ROBOTICS, INC.
To: TRINITY CAPITAL INC.
Reel/Frame 074519/0513 →
SECURITY INTEREST Recorded Nov 10, 2025
From: PATH ROBOTICS, INC.
To: TRIPLEPOINT PRIVATE VENTURE CREDIT INC.
Reel/Frame 072844/0367 →
SECURITY INTEREST Recorded Oct 3, 2024
From: PATH ROBOTICS, INC.
To: TRIPLEPOINT PRIVATE VENTURE CREDIT INC.
Reel/Frame 068789/0005 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 6, 2024
From: LONSBERRY, ALEXANDER; LONSBERRY, ANDREW; AJAM GARD, NIMA; VASU, MADHAVUN CANDADAI; SCHWENKER, ERIC
To: PATH ROBOTICS, INC.
Reel/Frame 067323/0993 →
Continuity (2)
Provisional Application 63281573 · Nov 19, 2021
Related Publication 20230173676A1 · Jun 8, 2023
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