Determination of Task Plans for Robotic Devices

Patent metadata

Field	Value
Publication number	US-2026001224-A1
Application number	US-202519256118-A
Country	US
Kind code	A1
Filing date	Jun 30, 2025
Priority date	Jun 28, 2024
Publication date	Jan 1, 2026
Grant date	—

Abstract

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

First claim

1 . A method for determining a task plan that is usable by a robotic device in a workspace, comprising:
converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton; generating a task probabilistic machine learning model with feasible task plans using the non-deterministic Buchi Automaton; generating a plurality of task plans using the task probabilistic machine learning model; constructing a sensor probabilistic machine learning model of the workspace using information from sensors of the robotic device; and comparing the task plans from task probabilistic machine learning model with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

2 . The method as in claim 1 , wherein comparing the plurality of task plans from the task probabilistic machine learning model with the sensor probabilistic machine learning model is performed using Bayesian inference with a defined probability threshold.

3 . The method as in claim 1 , further comprising:
modifying the task plan to correct for deficiencies as compared to stored successful task plans, workspace data, or non-visible markings on objects.

4 . The method as in claim 1 , further comprising:
creating a learning data store with successful task plans completed by a human; and comparing the task plan selected to stored successful task plans to determine whether to execute the task plan.

5 . The method as in claim 4 , wherein a successful task plan is defined as successful based in part on completion by a user using the robotic device.

6 . The method as in claim 4 , wherein a plurality of successful task plans may define a variation envelope for a successful plan type.

7 . The method as in claim 6 , further comprising comparing the task plan selected with the variation envelope to determine whether to execute the task plan.

8 . The method as in claim 1 , further comprising receiving a sequence of actions through instructions in a domain-specific language.

9 . The method as in claim 1 , wherein the instructions are received using at least one of: a verbal query, a text query, or a graphical query.

10 . The method as in claim 1 , wherein the temporal logic (TL) statements comprise states, actions, and predicates.

11 . The method as in claim 1 , wherein the non-deterministic Buchi Automaton includes states and a transition function to modify states based on actions and predicates.

12 . The method as in claim 1 , wherein the sensor probabilistic machine learning model classifies objects and estimates poses of objects in the workspace.

13 . The method as in claim 12 , wherein the sensor probabilistic machine learning model can classify objects that need to be manipulated by the robotic device and objects that need to be avoided by the robotic device during task completion.

14 . The method as in claim 1 , wherein the task plan is executed by the robotic device.

15 . The method as in claim 1 , further comprising modifying a pose of an object that is not identifiable using a robotic device sensor in order to allow the object to be recognized.

16 . The method as in claim 1 , further comprising:
identifying non-visible markings on objects using a robotic device sensor; and comparing the non-visible markings to recorded non-visible markings stored in a learning data store of successful task plans in order to determine whether a task plan is executing correctly.

17 . The method as in claim 1 , further comprising:
identifying non-visible markings on objects using a robotic device sensor; and comparing the non-visible markings with stored non-visible marking in order to modify a command for carrying out the task plan.

18 . A method for determining a task plan that is usable by a robotic device in a workspace, comprising:
converting instructions received for the robotic device into temporal logic (TL) statements and then to a non-deterministic Buchi Automaton; generating a task probabilistic machine learning model with feasible task plans using the non-deterministic Buchi Automaton; generating a plurality of task plans using the task probabilistic machine learning model; constructing a sensor probabilistic machine learning model of the workspace using information from sensors of the robotic device; creating a learning data store with successful task plans completed by a human using the robotic device; comparing the task probabilistic machine learning model and the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace; comparing the task plan selected to successful task plans or workspace data to determine whether to modify the task plan to improve the task plan; and executing the task plan using the robotic device.

19 . The method as in claim 18 , wherein a successful task plan is defined as successful based in part on completion by a user using the robotic device.

20 . The method as in claim 18 , wherein a plurality of successful task plans may define a variation envelope for a successful plan type.

21 . The method as in claim 20 , further comprising comparing the task plan selected with the variation envelope to determine whether to execute the task plan.

22 . The method as in claim 18 , further comprising modifying the task plan to correct for deficiencies as compared to stored successful task plans or workspace data.

23 . The method as in claim 18 , wherein comparing the task probabilistic machine learning model and the sensor probabilistic machine learning model is performed using Bayesian inference with a defined probability threshold.

24 . The method as in claim 18 , further comprising receiving a sequence of actions through instructions in a domain-specific language.

25 . The method as in claim 18 , wherein the instructions are received and a successful task plan is defined as successful based in part on completion by a user using the robotic device.

26 . The method as in claim 18 , wherein the temporal logic (TL) statements comprise states, actions and predicates.

27 . The method as in claim 18 , wherein the non-deterministic Buchi Automaton includes states and a transition function to modify states based on actions and predicates.

28 . The method as in claim 18 , wherein the sensor probabilistic machine learning model classifies objects and estimates poses of objects in the workspace.

29 . The method as in claim 28 , wherein the sensor probabilistic machine learning model can classify objects that need to be manipulated by a robotic device and objects that need to be avoided by a robotic device during task completion.

30 . The method as in claim 18 , further comprising:
identifying non-visible markings on objects using a robotic device sensor; and comparing the non-visible markings to recorded non-visible markings stored in the learning data store of successful task plans in order to determine whether a task plan is executing correctly.

31 . The method as in claim 18 , further comprising:
identifying non-visible markings on objects using a robotic device sensor; and comparing the non-visible markings with stored non-visible marking in order to modify a command for carry

Assignees

• Sarcos Corp

Inventors

• Garagic Denis
• Smith Fraser M
• Olivier Marc X

Classifications

• G05B2219/40395

  Compose movement with primitive movement segments from database · CPC title

• G05B2219/40393

  Learn natural high level command, associate its template with a plan, sequence · CPC title

• G05B2219/40116

  Learn by operator observation, symbiosis, show, watch · CPC title

• G05B2219/39244

  Generic motion control operations, primitive skills each for special task · CPC title

• B25J9/163

  learning, adaptive, model based, rule based expert control · CPC title