Office Action 3: Notice of Allowance (2025-03-26)

CLAIMS:
Amendments to the Claims

This listing of claims will replace all prior versions of claims in the application:

Listing of Claims:

1\. (Currently Amended) A method of operation of a fleet of robots, the method comprising:
identifying, by a first robot in the fleet of robots, a set of candidate actions that may be performed by a first robot;
collecting, by at least one sensor onboard the first robot and for each candidate action of the set of candidate actions, a respective set of sensor data;
transmitting, by the first robot, a request for instructions to a tele-operation system, the tele-operation system communicatively coupled to the fleet of robots, the request for the instructions which includes each candidate action of the set of candidate actions and each respective set of sensor data;
receiving, by the first robot, the instructions from the tele-operation system;
executing, by the first robot, the instructions;
updating, by the first robot, a first control model of the first robot, based at least in part on each candidate action of the set of candidate actions, each respective set of sensor data, and the instructions, to increase a level of autonomy of the first robot;
sendinq, by the first robot and directly to a second robot in the fleet of robots, , control model update instructions based on the update made to the first control model; and
updating, by a-the second robot , a second control model of the second robot, based at least in part on the update made to the first control model of the first robot, to increase a level of autonomy of the second robot, wherein the second control model of the second robot is different from the first control model of the first robot.

2\. (Currently Amended) The method of claim 1 wherein the identifying a set of candidate actions that may be performed by the first robot includes sensing, by the at last one sensor onboard the first robot, at least one of an external event or an external condition and identifying the set of candidate actions in response to sensing the at least one of an external event or an external condition.

3\. (Previously Presented) The method of claim 1, further comprising receiving, by the first robot and from the tele-operation system, an objective to be completed by the first robot, wherein the identifying a set of candidate actions that may be performed by the first robot includes identifying a set of candidate actions that may be performed by the first robot in furtherance of the objective.

4\. (Previously Presented) The method of claim 3, wherein the transmitting a request for instructions to a tele-operation system includes transmitting the request for instructions to the tele-operation system in response to determining the first robot is unable to select a candidate action of the set of candidate actions to perform in furtherance of the objective.

5\. (Previously Presented) The method claim 4, further comprising scoring, by the first robot, each candidate action of the set of candidate actions, wherein the scoring each candidate action of the set of candidate actions includes assigning a respective probability to each candidate action, the respective probability being based at least in part on a respective expectation of progress, achievable by each candidate action, towards the objective.

6\. (Previously Presented) The method of claim 5, further comprising ranking, by the first robot, each candidate action in an order of preference, the order of preference being based at least in part on the respective probability of each candidate action.

7\. (Previously Presented) The method of claim 6, wherein the transmitted request for instructions includes each respective probability and the order of preference.

8\. (Previously Presented) The method of claim 6, further comprising determining, by the first robot, whether a respective probability meets a predetermined probability criterion.

9\. (Previously Presented) The method of claim 6, further comprising determining, by the first robot, whether a difference between a first respective probability and a second respective probability meets a predetermined difference criterion.

10\. (Currently Amended) The method of claim 1, the first robot being a semi- autonomous robot characterized by a level of autonomy, the level of autonomy being determined at least in part by the first control model, the method further comprising comparing the level of autonomy of the first robot to a threshold level of autonomy, the threshold level of autonomy above which the first robot is operable to autonomously select a candidate action of the set of candidate actions to perform, wherein the transmitting, by the first robot, a request for instructions to a tele-operation system includes transmitting the request for instructions to the tele-operation system in response to determining the level of autonomy of the first robot is lower than the threshold level of autonomy.

11\. (Previously Presented) The method of claim 10, further comprising, in response to determining the level of autonomy of the first robot meets or exceeds the threshold level of autonomy, performing, by the first robot, one of the candidate actions of the set of candidate actions.

12\. (Previously Presented) The method of claim 11, further comprising receiving, by the first robot and from the tele-operation system, an objective to be completed by the first robot, wherein the identifying a set of candidate actions that may be performed by the first robot includes identifying a set of candidate actions that may be performed by the first robot in furtherance of the objective.

13\. (Previously Presented) The method of claim 12, further comprising determining, by the first robot, the threshold level of autonomy, wherein the determining the threshold level of autonomy includes assigning a respective probability to each candidate action, the respective probability being based at least in part on a respective expectation of progress, achievable by each candidate action, towards the objective.

14\. (Previously Presented) The method of claim 13, further comprising ranking, by the first robot, each candidate action in an order of preference, the order of preference being based at least in part on the respective probability.

15\. (Previously Presented) The method of claim 14, wherein the determining the level of autonomy of the first robot is lower than the threshold level of autonomy
includes determining the respective probability of each candidate action in the set of candidate actions fails to satisfy at least one predetermined criterion.

16\. (Currently Amended) The method of claim 1,the first robot comprising a data repository, and the method further comprising: extracting, for each candidate action of the set of candidate actions, a respective set of ancillary data from the data repository.

17\. (Previously Presented) The method of claim 1, wherein the transmitting, by the first robot, a request for instructions to a tele-operation system includes transmitting a request for the tele-operation system to determine which candidate action the first robot should perform.

18\. (Previously Presented) The method of claim 1, the first robot comprising a motion actuator, wherein executing the instructions includes causing, by the motion actuator, the first robot to execute a movement.

19\. (Currently Amended) The method of claim 1, wherein the updating, by the first robot, a firstcontrol model, based at least in part on each candidate action of the set of candidate actions, each respective set of sensor data, and the instructions includes increasing a level of autonomy of the first robot from a first level of autonomy to a second level of autonomy, the second level of autonomy being higher than the first level of autonomy.

20\. (Previously Presented) The method of claim 1, further comprising identifying, by the first robot, an objective to be completed by the first robot, wherein the identifying a set of candidate actions that may be performed by the first robot includes identifying a set of candidate actions that may be performed by the first robot in furtherance of the objective.

OFFICE ACTION:
Acknowledgement
This Notice of Allowance is in response to amendments filed 1/25/2025.
EXAMINER'S AMENDMENT
An examiner’s amendment to the record appears below. Should the changes and/or additions be unacceptable to applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
The application has been amended as follows:
In the sixteenth line of claim 1, the last comma in the sixteenth line of claim 1 has been deleted, such that only one comma is provided after the limitation of “the fleet of robots.”
In the sixth line of claim 16, the limitation of “ancillary” has been deleted, and the limitation of --sensor-- has been inserted.
Reasons for Allowance
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
The closest prior art of record, Duan et al. (CN 105773615 A), taken alone or in combination, does not teach the claimed method of operation of a fleet of robots, the method comprising:
identifying, by a first robot in the fleet of robots, a set of candidate actions that may be performed by a first robot;
collecting, by at least one sensor onboard the first robot and for each candidate action of the set of candidate actions, a respective set of sensor data;
transmitting, by the first robot, a request for instructions to a tele-operation system, the tele-operation system communicatively coupled to the fleet of robots, the request for the instructions which includes each candidate action of the set of candidate actions and each respective set of sensor data;
receiving, by the first robot, the instructions from the tele-operation system;
executing, by the first robot, the instructions;
updating, by the first robot, a first control model of the first robot, based at least in part on each candidate action of the set of candidate actions, each respective set of sensor data, and the instructions, to increase a level of autonomy of the first robot;
sending, by the first robot and directly to a second robot in the fleet of robots, control model update instructions based on the update made to the first control model; and
updating, by the second robot, a second control model of the second robot, based at least in part on the update made to the first control model of the first robot, to increase a level of autonomy of the second robot, wherein the second control model of the second robot is different from the first control model of the first robot.
Specifically, upon further search and consideration of the amendments filed 1/25/2025, Duan et al. has been identified as the closest relevant prior art. Duan et al. teaches a similar method of operation of a fleet of robots (see ¶0081-0086, regarding a particular example in which a robot receives instructions from a robot integrated control platform, where all processes and results are uploaded by the robot to the robot integrated control platform for reference by other robots), the method comprising identifying, by a first robot in the fleet of robots, a set of candidate actions that may be performed by a first robot (see ¶0083, regarding the robot decomposes a task), collecting, by at least one sensor onboard the first robot and for each candidate action of the set of candidate actions, a respective set of sensor data (see ¶0083, regarding that the robot determines conditions, e.g., sound, light, electricity, humidity, etc., defined as being detected by sensors in ¶0053), transmitting, by the first robot, a request for instructions to a tele-operation system (see ¶0083, regarding the robot sends a request to the robot integrated control platform based on the conditions), the tele-operation system communicatively coupled to the fleet of robots (see ¶0011, ¶0017, regarding the communication modules provided for communication between the robot and the integrated control platform, where other robots can access stored actions flows and results via the robot integrated control platform, as described in ¶0065), receiving, by the first robot, the instructions from the tele-operation system (see ¶0084, regarding that instructions are sent to the robot by the robot integrated control platform), executing, by the first robot, the instructions (see ¶0085, regarding that all received instructions are executed by the robot), and updating, by the first robot, a first control model of the first robot, based at least in part on each candidate action of the set of candidate actions, each respective set of sensor data, and the instructions, to increase a level of autonomy of the first robot (see ¶0063-0064, regarding the self-learning module of the robot downloads the action flow of the robot’s task execution and the action results corresponding to the action flow stored in it from the robot integrated control platform, thereby improving the performance of the robot). While Duan et al. teaches that the robot transmits its own stored action flow for completing the task and action results corresponding to the action flow to the robot integrated control platform to be shared with other robots (see ¶0065), Duan et al. does not teach sending, by the first robot and directly to a second robot in the fleet of robots, control model update instructions based on the update made to the first control model, and updating, by the second robot, a second control model of the second robot, based at least in part on the update made to the first control model of the first robot, to increase a level of autonomy of the second robot, wherein the second control model of the second robot is different from the first control model of the first robot, as claimed. Further, Duan et al. does not teach that the request for the instructions includes each candidate action of the set of candidate actions and each respective set of sensor data, as claimed.
Additional relevant prior art identified in the PTO-892 form does not teach the claimed invention, including Boca et al. (WO 2016/172718 A1) that teaches the known technique of transmitting updated models between robot systems (see ¶0040), Rajkumar et al. (US 10,730,181 B1) that teaches the selective sharing of learned information among specific robots in a fleet based on roles (see col. 10, lines 1-8), and von Reventlow et al. (US 2023/0168670 A1) that teaches updating a database by an operator, so as to make improved evaluation of action definition candidates, reconsider segmentation of the task, or selection of action definition candidates that improve a robot’s capability of autonomously executing a task (see ¶0079).
No reasonable combination of prior art can be made to teach the claimed invention. The claimed invention would not have been obvious to one of ordinary skill in the art before the effective filing date.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

APPLICANT REBUTTAL: REMARKS
This is a Response to the Final Office Action mailed October 25, 2024 (the "Final Office Action"), in which a three (3) month Shortened Statutory Period for Response has been set, due to expire January 25, 2025. By way of this Response, claims 1, 2, 10, 16, and 19 are amended. No claims are canceled and no new claims are added. No new matter has been added to the application. Claims 1-20 are pending.
Claim Objections
Claim 1 was objected to due to an typographical informality, for which the
Examiner proposed a re-wording to resolve. Applicant has amended claim 1 in accordance with the Examiner's suggestion and therefore requests that the objection to claim 1 be withdrawn.
Rejections Under 35 USC 103
Claims 1-20 were rejected under 35 USC 103 for being allegedly unpatentable over US 20230168670 (hereafter, "von Reventlow") in view of US 20200150674 (hereafter, "Ferguson").
Applicant does not acquiesce to the Examiner's rejections of claims 1 -20, but in the interest of expediting allowance of the instant application Applicant has amended independent claim 1 to further distinguish from von Reventlow. Specifically, Applicant has acknowledged, with thanks, the Examiner's suggestion on page 2-3 of the Final Office Action that "[t]he application of prior art may be overcome by providing amendments that further limit "ancillary data" to be specifically sensor data acquired from sensor onboard the "first robot," in light of the embodiments described on page 26 of the specification filed 4/12/2022, as well as amendments defining the "control model of the first robot" to be different from the "control model of the second robot," such that the control models of each robot are not synchronized with a remote database, as taught by von Reventlow, but updated via peer-to-peer communication, as described in the Applicant's disclosure" (Final Office Action, pages 2-3). Applicant has amended independent claim 1 to include all the features suggested by the Examiner, and further amended dependent claims 2, 10, 16, and 19 simply to render the language therein consistent with the amendments made to independent claim 1.
Applicant respectfully submits that von Reventlow, in further view of Ferguson, does not teach or suggest all of the features of any of Applicant's amended claims, and therefore requests that the rejections of claims 1-20 under 35 USC 103 be withdrawn.
Conclusion


CITED REFERENCE SUMMARIES: Ref US10730181B1: Claims:
A method comprising: storing, by a robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots; receiving, by the robot, a second machine learning model from a server system over a communication network; generating, by the robot, a second private embedding for each of the one or more first private embeddings in the cache, wherein each of the second private embeddings is generated using the second machine learning model; adding, by the robot, the second private embeddings to the cache of the robot; and removing, by the robot, the one or more first private embeddings from the cache of the robot.
storing, by a robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots;
receiving, by the robot, a second machine learning model from a server system over a communication network;
generating, by the robot, a second private embedding for each of the one or more first private embeddings in the cache, wherein each of the second private embeddings is generated using the second machine learning model;
adding, by the robot, the second private embeddings to the cache of the robot; and
removing, by the robot, the one or more first private embeddings from the cache of the robot.
The method of claim 1 , wherein the first embeddings include one or more public embeddings that are shared with a server system; and wherein the method further comprises: determining that a particular embedding is a public embedding; and after receiving the second machine learning model, removing the particular embedding from the cache of the robot based on determining that the particular embedding is a public embedding.
wherein the method further comprises: determining that a particular embedding is a public embedding; and after receiving the second machine learning model, removing the particular embedding from the cache of the robot based on determining that the particular embedding is a public embedding.
determining that a particular embedding is a public embedding; and
after receiving the second machine learning model, removing the particular embedding from the cache of the robot based on determining that the particular embedding is a public embedding.
The method of claim 1 , further comprising storing, by the robot, sensor data used to generate the one or more first private embeddings; wherein generating the second private embedding for each of the one or more first private embeddings in the cache comprises generating, using the second machine learning model, each of the second private embeddings from the stored sensor data used to generate the one or more first private embeddings; and wherein the method further comprises storing the second private embeddings in the cache of the robot.
wherein generating the second private embedding for each of the one or more first private embeddings in the cache comprises generating, using the second machine learning model, each of the second private embeddings from the stored sensor data used to generate the one or more first private embeddings; and
wherein the method further comprises storing the second private embeddings in the cache of the robot.
The method of claim 3 , wherein generating the second private embeddings from the stored sensor data comprises generating a second private embedding for an object by inputting, to the second machine learning model, feature values derived from the sensor data used to generate the first private embedding for the object.
The method of claim 1 , further comprising: accessing, by the robot, metadata associated with a particular embedding of the first embeddings; and determining that the particular embedding is a private embedding that is not shared with the server system by analyzing the metadata associated with the particular embedding using one or more sharing rules that are stored by the robot.
accessing, by the robot, metadata associated with a particular embedding of the first embeddings; and
determining that the particular embedding is a private embedding that is not shared with the server system by analyzing the metadata associated with the particular embedding using one or more sharing rules that are stored by the robot.
The method of claim 1 , wherein the first embeddings include one or more first public embeddings in addition to the one or more first private embeddings, and the method further comprises providing, by the robot, the one or more first public embeddings to the server system and not providing the one or more first private embeddings to the server system.
the method further comprises providing, by the robot, the one or more first public embeddings to the server system and not providing the one or more first private embeddings to the server system.
The method of claim 6 , further comprising determining, for each of the first embeddings, whether to store sensor data used to generate the first embedding, wherein the determination whether to store sensor data used to generate a first embedding is based on whether the first embedding is private or public; based on the determining, storing, by the robot, sensor data for each of the one or more first private embeddings, and not storing sensor data for the one or more first public embeddings.
based on the determining, storing, by the robot, sensor data for each of the one or more first private embeddings, and not storing sensor data for the one or more first public embeddings.
The method of claim 1 , wherein the first embeddings generated by a first machine learning model include one or more received embeddings that were received by the robot from the server system over a communication network, and the method further comprises: determining that a particular embedding of the first embeddings is one of the received embeddings; and after receiving the second machine learning model, removing the particular embedding from the cache based on determining that the particular embedding is one of the received embeddings.
the method further comprises: determining that a particular embedding of the first embeddings is one of the received embeddings; and after receiving the second machine learning model, removing the particular embedding from the cache based on determining that the particular embedding is one of the received embeddings.
determining that a particular embedding of the first embeddings is one of the received embeddings; and
after receiving the second machine learning model, removing the particular embedding from the cache based on determining that the particular embedding is one of the received embeddings.
The method of claim 1 , wherein the cache is a local cache stored on a data storage device of the robot.
The method of claim 1 , wherein the first machine learning model is a first neural network model, and wherein the second machine learning model is a second neural network model.
The method of claim 1 , further comprising: obtaining, by the robot, sensor data describing an object; generating, by the robot, an embedding for the object based on the sensor data; determining, by the robot, whether to designate the embedding as a private embedding or a public embedding; and storing, by the robot, the embedding and data designating whether the embedding is a private embedding or a public embedding.
obtaining, by the robot, sensor data describing an object;
generating, by the robot, an embedding for the object based on the sensor data;
determining, by the robot, whether to designate the embedding as a private embedding or a public embedding; and
storing, by the robot, the embedding and data designating whether the embedding is a private embedding or a public embedding.
The method of claim 11 , wherein determining whether to designate the embedding as a private embedding or a public embedding comprises: determining a location of the object or the robot; determining that the location is within a predetermined area for which information is not shared; and based on determining that the location is within the predetermined area, designating the embedding as a private embedding.
determining a location of the object or the robot;
determining that the location is within a predetermined area for which information is not shared; and
based on determining that the location is within the predetermined area, designating the embedding as a private embedding.
The method of claim 11 , wherein determining whether to designate the embedding as a private embedding or a public embedding comprises: determining an object type for the object observed by the robot; determining that the object type is one of a predetermined set of object types; and determining whether to designate the embedding as a private embedding or a public embedding based on determining that the object type is one of the predetermined set of object types.
determining an object type for the object observed by the robot;
determining that the object type is one of a predetermined set of object types; and
determining whether to designate the embedding as a private embedding or a public embedding based on determining that the object type is one of the predetermined set of object types.
The method of claim 11 , wherein determining whether to designate the embedding as a private embedding or a public embedding comprises: accessing a sharing rule indicating one or more restrictions on sharing of information obtained by the robot, wherein the one or more restrictions limit sharing of information (i) observed by the robot at a time or place, or (ii) describing objects having an object type or object characteristic; determining, based on the sensor data for the object, that the sharing rules restrict sharing of information for the object described by the sensor data; and based on determining that the sharing rules restrict sharing of information for the object, designating the embedding for the object as a private embedding.
accessing a sharing rule indicating one or more restrictions on sharing of information obtained by the robot, wherein the one or more restrictions limit sharing of information (i) observed by the robot at a time or place, or (ii) describing objects having an object type or object characteristic;
determining, based on the sensor data for the object, that the sharing rules restrict sharing of information for the object described by the sensor data; and
based on determining that the sharing rules restrict sharing of information for the object, designating the embedding for the object as a private embedding.
The method of claim 11 , further comprising receiving user input indicating one or more criteria comprising a time, place, object type, or object characteristic for which sharing of information is restricted; and storing a sharing rule that designates embeddings for objects meeting the one or more criteria as private embeddings; wherein determining whether to designate the embedding as a private embedding or a public embedding comprises determining whether the embedding satisfies the one or more criteria.
storing a sharing rule that designates embeddings for objects meeting the one or more criteria as private embeddings;
wherein determining whether to designate the embedding as a private embedding or a public embedding comprises determining whether the embedding satisfies the one or more criteria.
A robot comprising: one or more processors; and one or more machine-readable storage devices storing instructions that, when executed by the one or more processors, cause the robot to perform operations comprising: storing, by the robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots; receiving, by the robot, a second machine learning model from a server system over a communication network; generating, by the robot, a second private embedding for each of the one or more first private embeddings in the cache, wherein each of the second private embeddings is generated using the second machine learning model; adding, by the robot, the second private embeddings to the cache of the robot; and removing, by the robot, the one or more first private embeddings from the cache of the robot.
one or more processors; and
one or more machine-readable storage devices storing instructions that, when executed by the one or more processors, cause the robot to perform operations comprising: storing, by the robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots; receiving, by the robot, a second machine learning model from a server system over a communication network; generating, by the robot, a second private embedding for each of the one or more first private embeddings in the cache, wherein each of the second private embeddings is generated using the second machine learning model; adding, by the robot, the second private embeddings to the cache of the robot; and removing, by the robot, the one or more first private embeddings from the cache of the robot.
storing, by the robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots;
The robot of claim 16 , wherein the first embeddings include one or more public embeddings that are shared with a server system; and wherein the operations further comprise: determining that a particular embedding is a public embedding; and after receiving the second machine learning model, removing the particular embedding from the cache of the robot based on determining that the particular embedding is a public embedding.
wherein the operations further comprise: determining that a particular embedding is a public embedding; and after receiving the second machine learning model, removing the particular embedding from the cache of the robot based on determining that the particular embedding is a public embedding.
The robot of claim 16 , wherein the operations further comprise storing, by the robot, sensor data used to generate the one or more first private embeddings; wherein generating the second private embedding for each of the one or more first private embeddings in the cache comprises generating, using the second machine learning model, each of the second private embeddings from the stored sensor data used to generate the one or more first private embeddings; and wherein the operations further comprise storing the second private embeddings in the cache of the robot.
wherein the operations further comprise storing the second private embeddings in the cache of the robot.
The robot of claim 18 , wherein generating the second private embeddings from the stored sensor data comprises generating a second private embedding for an object by inputting, to the second machine learning model, feature values derived from the sensor data used to generate the first private embedding for the object.
One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause a robot to perform operations comprising: storing, by the robot, first embeddings generated using a first machine learning model, wherein the robot stores the first embeddings in a cache of the robot, wherein the first embeddings include one or more first private embeddings that are not shared with other robots; receiving, by the robot, a second machine learning model from a server system over a communication network; generating, by the robot, a second private embedding for each of the one or more first private embeddings in the cache, wherein each of the second private embeddings is generated using the second machine learning model; adding, by the robot, the second private embeddings to the cache of the robot; and removing, by the robot, the one or more first private embeddings from the cache of the robot.
Ref US20230168670A1: Claims:
A service robot system, comprising a robot, wherein the robot ( 1 ) having a drive system ( 3 ) for moving the robot ( 1 ) to target positions and at least one effector ( 4 ) for manipulating the robot's environment, wherein the service robot system comprises a processing unit ( 14 ) including a task determination unit for determining a task to be executed by the robot and configured to control the drive system ( 3 ) and the at least one effector ( 4 ) according to the task based on action definitions, wherein the processing unit ( 14 ) is configured to automatically retrieve action definition candidates from a database ( 17 ), evaluate the retrieved action definition candidates with respect to a success score indicating a likelihood that an action according to the action definition candidate contributes to successfully fulfilling the task, to execute an action according to the action definition candidate having highest probability equal to or above a predefined threshold, and to send a request for assistance via a communication interface ( 21 ) if the success score is less than a preset threshold.
the robot ( 1 ) having a drive system ( 3 ) for moving the robot ( 1 ) to target positions and at least one effector ( 4 ) for manipulating the robot's environment, wherein the service robot system comprises a processing unit ( 14 ) including a task determination unit for determining a task to be executed by the robot and configured to control the drive system ( 3 ) and the at least one effector ( 4 ) according to the task based on action definitions, wherein the processing unit ( 14 ) is configured to automatically retrieve action definition candidates from a database ( 17 ), evaluate the retrieved action definition candidates with respect to a success score indicating a likelihood that an action according to the action definition candidate contributes to successfully fulfilling the task, to execute an action according to the action definition candidate having highest probability equal to or above a predefined threshold, and to send a request for assistance via a communication interface ( 21 ) if the success score is less than a preset threshold.
The service robot system according to claim 1 , wherein the processing unit ( 14 ) is configured to determine the success score for an action definition candidate based on information on success of an operation of an action according to the respective action definition candidate in the past.
The service robot system according to claim 1 , wherein the processing unit ( 14 ) is configured to segment the task into a sequence of action definitions.
The service robot system according to claim 1 , wherein the robot ( 1 ) is configured to execute an action that has a
The service robot system according to claim 1 , wherein the processing unit ( 14 ) is configured to dynamically predict potential changes of the environment and to adapt actions, or select actions.
The service robot system according to claim 1 , further comprising an operator interface ( 18 , 19 , 20 ) for remotely controlling the robot ( 1 ) to execute an action and/or input information for enhancing the database ( 17 ) by the operator interface in response to the request.
The service robot system according to claim 6 , wherein a definition of the action for remotely controlling is stored in the database ( 17 ) as a new action defi
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