Office Action 1: Non-Final Rejection (2024-09-06)
Rejection types: 102 - Lack of Novelty, 103 - Obviousness

CLAIMS:
CLAIMS

What is claimed is:

1\. A robot motion control method, applied to a robot, the method comprising:
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion;
the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
obtaining, based on optimization of an objective function, center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move;
generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and
controlling the robot to execute the target motion based on the joint control information.

2\. The method according to claim 1, wherein a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory, and the obtaining the center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move comprises:
obtaining desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot; and
calculating, based on the desired center-of-mass motion data, the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.
3\. The method according to claim 1, wherein the objective function is: SVG {EMBED HERE} 0.21 2.16
where W, denotes a positive definite weight matrix, wdes denotes the desired center-of- mass motion data, A denotes a matrix constructed based on a position relationship between the center of mass of the robot and contact points, and f denotes the center-of-mass control information, the contact points being contact points between the robot and an environment.

4\. The method according to claim 2, wherein the calculating the center-of-mass control information comprises:
utilizing quadratic programming (QP) optimization to calculate the center-of-mass control information.
5\. The method according to claim 2, wherein the calculating the center-of-mass control information comprises:
utilizing interior point optimizer (IPOPT) optimization to calculate the center-of-mass control information.

6\. The method according to claim 2, wherein the obtaining the desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory comprises:
obtaining actual center-of-mass motion data of the robot;
obtaining reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory;
obtaining a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data; and
obtaining the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.

7\. The method according to claim 6, wherein the obtaining the center-of-mass motion data adjustment value according to the difference between the actual center-of-mass motion data and the reference center-of-mass motion data comprises:
calculating a difference between the actual center-of-mass motion data and the reference center-of-mass motion data; and
obtaining the center-of-mass motion data adjustment value according to a gain matrix and the difference, the gain matrix corresponding to the motion data associated with the center of mass of the robot.

8\. The method according to claim 6, wherein the center-of-mass motion data adjustment value comprises a center-of-mass acceleration adjustment value and a center-of-mass angular acceleration adjustment value.

9\. The method according to claim 8, wherein the center-of-mass acceleration adjustment value is represented by padj = Kd(pref\_pact) + Kp(prefpact) where padj denotes a center-of-mass acceleration adjustment value, pref denotes a reference center-of-mass velocity, pact denotes an actual center-of-mass velocity, pref denotes a reference center-of-mass position, pact denotes an actual center-of-mass position, Kd and Kp denote gain matrices associated with center-of-mass acceleration.
10\. The method according to claim 8, wherein the center-of-mass angular acceleration adjustment value is represented by 6adj = Kd(oref-act) + Kp(RrefRact)v, where 6adj denotes a center-of-mass angular acceleration adjustment value, oref denotes a reference center-of-mass angular velocity, oact denotes an actual center-of-mass angular velocity, Rref denotes a reference rotation matrix of a center-of-mass coordinate system relative to a world coordinate system, Ract denotes an actual rotation matrix of the center-of-mass coordinate system relative to the world coordinate system, (RrefRact)v denotes a rotation vector from Ract to Rref, and Kp and Kd denote gain matrices associated with center-of-mass angular acceleration.

11\. The method according to claim 1, wherein the method further comprises:
performing kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and
analyzing the kinetics equation and constructing the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.

12\. The method according to claim 1, wherein the generating the joint control information according to the center-of-mass control information and the structure matrix of the robot comprises:
generating intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot;
obtaining actual joint motion data of the robot;
obtaining reference joint motion data of the robot; and
adjusting the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.

13\. The method according to claim 12, wherein the joint control information comprises joint torque represented by T = -Jfopt + K (grefpact) + Kp(grefgact), where T denotes joint torque, J, denotes the structure matrix corresponding to the robot, fopt denotes the center-of-mass control information obtained by optimization, gref denotes a reference joint angular velocity, gact denotes an actual joint angular velocity, gref denotes a reference joint angle, qact denotes an actual joint angle, and Kp and Kd are gain matrices associated with the joint torque.

14\. The method according to claim 13, wherein the robot is a quadruped robot, and the target motion is a motion process from a four-legged lying down state to a two-legged standing state.
15\. A robot motion control apparatus, comprising:
a memory operable to store computer-readable instructions; and
a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to:
obtain a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
obtain, based on optimization of an objective function, center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move;
generate joint control information according to the center-of-mass control information and a structure matrix of the robot; and
control the robot to execute the target motion based on the joint control information.

16\. The apparatus according to claim 15, wherein a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory, and the processor circuitry is configured to:
obtain desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot; and
calculate, based on the desired center-of-mass motion data, the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.

17\. The apparatus according to claim 16, wherein the processor circuitry is configured to:
obtain actual center-of-mass motion data of the robot;
obtain reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory;
obtain a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data; and
obtain the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.

18\. The apparatus according to claim 15, wherein the processor circuitry is further configured to: perform kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and
analyze the kinetics equation and construct the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.

19\. The apparatus according to claim 15, wherein the processor circuitry is configured to:
generate intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot;
obtain actual joint motion data of the robot;
obtain reference joint motion data of the robot; and
adjust the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.

20\. A non-transitory machine-readable media, having instructions stored on the machine- readable media, the instructions configured to, when executed, cause a machine to:
obtain a center-of-mass reference trajectory for guiding the robot to execute a target motion;
the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion;
obtain, based on optimization of an objective function, center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move;
generate joint control information according to the center-of-mass control information and a structure matrix of the robot; and
control the robot to execute the target motion based on the joint control information. 21PCT843/US27 21PCT843/US28 21PCT843/US29 21PCT843/US30 21PCT843/US31

OFFICE ACTION:
DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 742 - 748.  Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 772 - 778.  Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
*Note: Examiner assumes 742 – 748 and 772 – 778 are the same.  Just renumber the drawing or specification to match.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 15 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ha et al. (US Pub No: 2018/0107175 A1, hereinafter Ha).
Regarding Claim 1:
Ha discloses:
A robot motion control method, applied to a robot, the method comprising.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.  These trajectories are computing using trajectory optimization.
obtaining, based on optimization of an objective function, center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.  These trajectories are computing using trajectory optimization.
generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and controlling the robot to execute the target motion based on the joint control information.  Paragraph [0093] describes a Covariance Matrix Adaptation-Evolution Strategy.  Paragraph [0094] describes joint positions being sequentially solved in this matrix to find the joint positions including global position, orientation and joint angles at time t to match the desired trajectory of the motion optimizer.
Regarding Claim 15:
Ha discloses:
A robot motion control apparatus, comprising: a memory operable to store computer-readable instructions; and a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to.  Paragraph [0040] describes memory/data storage devices 150 and a processor 110.
The rest of claim 15 is substantially similar to claim 1 and is rejected on the same grounds.
Claim 20 is substantially similar to claim 15 and is rejected on the same grounds.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2, 4 – 5, 11, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke et al. (US Pub No: 2022/0184802 A1, hereinafter Rinke).
Regarding Claim 2:
Ha discloses the above inventions in claim 1.  Ha does not disclose a degree of deviation of the robot from the center-of-mass reference trajectory and calculating a satisfying condition to optimize a stop condition.
Rinke teaches:
The method according to claim 1, wherein a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory, and the obtaining the center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move comprises.  Paragraph [0085] describes a non-optimized piecewise linear motor control in which the compliant rod vibrates substantially around the target poses and deviates from the target motion significantly.  Paragraph [0089] and figure 9 describes how much the robot is deviating in graph 925, 935 and 945.
obtaining desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot.  Paragraph [0089] describes a Center-of-Mass (COM) deviation in graphs 925, 935 and 945 which describes COM motion data.
and calculating, based on the desired center-of-mass motion data, the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  Paragraph [0076] describes a differential-algebraic equations (DAE) to retarget motion profiles of the physical robot to minimize the dynamic equilibrium constant to minimize deviations.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Rinke to show a degree of deviation of the robot from the center-of-mass reference trajectory and calculating a satisfying condition to optimize a stop condition.  One would have been motivated to do so in order to yield fast and accurate vibration suppression of robot movement (Abstract of Rinke).
Claim 16 is substantially similar to claim 2 and is rejected on the same grounds.
Regarding Claim 11:
Ha discloses the above inventions in claim 1.  Ha does not disclose a kinetics equation obtained by simplification of the robot body and analyzing the equation to construct the objective function.
Rinke teaches:
The method according to claim 1, wherein the method further comprises: performing kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and analyzing the kinetics equation and constructing the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.  Paragraph [0147] and figure 17 illustrates data flow during learning of a robot controller.  The neural network performs learning algorithms using unfiltered control parameter 1720.  Kinematics 1730 provides target data over time and outputs a simulation state of the robotic system over time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Rinke to show a kinetics equation obtained by simplification of the robot body and analyzing the equation to construct the objective function.  One would have been motivated to do so in order to yield fast and accurate vibration suppression of robot movement (Abstract of Rinke).
Claim 18 is substantially similar to claim 11 and is rejected on the same grounds.
Regarding Claim 4:
Ha discloses:
The method according to claim 2, wherein the calculating the center-of-mass control information comprises: utilizing quadratic programming (QP) optimization to calculate the center-of-mass control information.  Paragraph [0056] describe Sequential Quadratic Programming (SQP) to provide motions of all the key parts of the robot.
Regarding Claim 5:
Ha discloses:
The method according to claim 2, wherein the calculating the center-of-mass control information comprises: utilizing interior point optimizer (IPOPT) optimization to calculate the center-of-mass control information.  Paragraph [0097] describes a interior-point method was used to minimize the iterations to converge and decrease the vibrations from the input sequence.
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and further in view of Chen et al. (US Pub No: 2021/0178587 A1, hereinafter Chen).
Regarding Claim 6:
Ha and Rinke teach the above inventions in claim 2.  Ha does not teach an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.
Chen teaches:
The method according to claim 2, wherein the obtaining the desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory comprises: obtaining actual center-of-mass motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
and obtaining the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha and Rinke to incorporate the teachings of Chen to show an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 17 is substantially similar to claim 6 and is rejected on the same grounds.
Claim(s) 7 – 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and Chen and further in view of Oh et al. (US Pub No: 2015/0367518 A1, hereinafter Oh).
Regarding Claim 7:
Chen teaches:
The method according to claim 6, wherein the obtaining the center-of-mass motion data adjustment value according to the difference between the actual center-of-mass motion data and the reference center-of-mass motion data comprises: calculating a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
Ha, Rinke and Chen do not teach a gain matrix associated with the center of mass of the robot.
Oh teaches
and obtaining the center-of-mass motion data adjustment value according to a gain matrix and the difference, the gain matrix corresponding to the motion data associated with the center of mass of the robot.  Paragraph [0071] describes a proportional gain of the COM position unit that represents a safety margin from an asymptotic value.  Paragraph [0102] describes that a proportional-differential controller changes the torque by using a COM Jacobian transposed matrix.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke and Chen to incorporate the teachings of Oh to show a gain matrix associated with the center of mass of the robot.  One would have been motivated to do so in order to tune a parameter and to get good approximations of complicated calculations.
Regarding Claim 8:
Rinke teaches:
The method according to claim 6, wherein the center-of-mass motion data adjustment value comprises a center-of-mass acceleration adjustment value and a center-of-mass angular acceleration adjustment value.  Paragraph [0066] describes a general inertial acceleration and angular acceleration.
Claim(s) 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Chen.
Regarding Claim 12:
Ha discloses the above inventions in claim 1.  Ha does not disclose intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.
Chen teaches:
The method according to claim 1, wherein the generating the joint control information according to the center-of-mass control information and the structure matrix of the robot comprises: generating intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining actual joint motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference joint motion data of the robot; and adjusting the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Chen to show an intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 19 is substantially similar to claim 12 and is rejected on the same grounds.
Allowable Subject Matter
Claims 3 and 13 – 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamane (US Pub No: 2016/0236349 A1): A control method, and a robot controller implementing the method, is provided that adapts human motions to floating-base humanoid robots with time warping techniques. The method of modifying a set of reference motions modifies the timeline of a reference motion so as to speed up or slow down one or more of the motions or motion segments. Through the use of time warping, the velocity and acceleration profiles of the motion are changed to turn an infeasible motion into a feasible one. The optimal time warping is obtained through a generalized motion feasibility index that quantifies the feasibility of a motion considering the friction constraint as well as the center-of-pressure (CoP) constraint. Due to the use of the motion feasibility index, the proposed motion adaptation method taught herein can be applied to motions on arbitrary terrains or with any number of links in contact with the environment.
LaValley (US Pub No: 2020/0369333 A1): A robot actor, or character mobility hardware platform, adapted to unleash or provide a wide variety of characters in the physical world. The robot actor enables the often screen-constrained characters to become life-like, interactive participants with nearby people in ways not presently achievable. The robot actor is an untethered, free-roaming robot that is has two (or more) legs, is adapted for high dexterity, is controlled and designed to be self-balancing, and, due to this combination of characteristics, the robot can provide characters with an illusion of life and, in many cases, in correct proportion and scale. The hardware and software of the robot actor will become a new generation of animatronic figures by providing a hardware platform capable of continuously evolving to become more capable through advances in controls and artificial intelligence (AI).


NEXT OFFICE ACTION: DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 3, 15 and 20 are amended.
Claims 1, 4 – 8, 11 - 12 and 15 and 17 – 20 are rejected.
Claims 3, 9 - 10 and 13 – 14 are objected
Claims 2 and 16 are cancelled.
Drawings
Applicant’s amendments to the drawings filed on 11/19/2024 have been accepted and overcome the previous objections.
Response to Arguments
Applicant’s amendments, mentioned in page 10 of the applicant’s arguments, have been fully considered and accepted.  They overcome the previous objections.
Applicant’s arguments on pages 10 – 11 have been fully considered but are moot due to the new grounds of rejection, Rinke and Huang.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4 – 5, 11, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ha et al. (US Pub No: 2018/0107175 A1, hereinafter Ha) in view of Rinke et al. (US Pub No: 2022/0184802 A1, hereinafter Rinke) and Huang (CN 111674486 A, hereinafter Huang).
*Translation and original included in IDS provided by applicant.
Regarding Claim 1:
Ha discloses:
A robot motion control method, applied to a robot, the method comprising.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.
obtaining a center-of-mass reference trajectory for guiding the robot to execute a target motion; the center-of-mass reference trajectory being a center-of-mass motion trajectory of the robot configured to achieve the target motion.  Paragraph [0037] describes a robot in which the trajectories of the robot are made using a center of mass of the robot and its feet.  These trajectories are computing using trajectory optimization.
generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and controlling the robot to execute the target motion based on the joint control information.  Paragraph [0093] describes a Covariance Matrix Adaptation-Evolution Strategy.  Paragraph [0094] describes joint positions being sequentially solved in this matrix to find the joint positions including global position, orientation and joint angles at time t to match the desired trajectory of the motion optimizer.
Ha does not disclose the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.
Rinke teaches:
determining desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory, the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot.  Paragraph [0089] describes a Center-of-Mass (COM) deviation in graphs 925, 935 and 945 which describes COM motion data.
calculating center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  Paragraph [0076] describes a differential-algebraic equations (DAE) to retarget motion profiles of the physical robot to minimize the dynamic equilibrium constant to minimize deviations.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha to incorporate the teachings of Rinke to show the desired center-of-mass motion data being a desired value of motion data associated with a center of mass of the robot and calculating the center-of-mass control information enabling the value of the objective function to satisfy an optimization stop condition.  One would have been motivated to do so in order to yield fast and accurate vibration suppression of robot movement (Abstract of Rinke).
Ha and Rinke do not teach constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.
Huang teaches:
constructing an objective function based on the desired center-of-mass motion data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points, the contact points being contact points between the robot and an environment, a value of the objective function is representing a degree of deviation of the robot from the center-of-mass reference trajectory.  Paragraph [0055] to [0060] and figure 2 describe controlling a stable walking of a biped robot.  In summary, step S104 describes constructing a center of mass state prediction model.  S106 describes simplifying the optimization equations of the center of mass prediction model.  Paragraph [0067] describes these equations having a center of mass, contact points of each supporting leg with the ground and a derivative of the rotation matrix.  Paragraph [0085] describes a weight vector of the center of mass state error at each moment.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date, with a reasonable expectation for success of the claimed invention, to have modified Ha and Rinke to incorporate the teachings of Huang to show constructing an object function based on center-of-mass rotation data and matrix data constructed based on a position relationship between the center of mass of the robot and contact points and a value of the objective function representing a degree of deviation of the robot from the center-of-mass reference trajectory.  One would have been motivated to do so to obtain control parameters for steady walking by solving the system of quadratic programming equations, i.e. the same role that it plays in the present application.
Regarding Claims 15 and 20:
Ha discloses:
A robot motion control apparatus, comprising: a memory operable to store computer-readable instructions; and a processor circuitry operable to read the computer-readable instructions, the processor circuitry when executing the computer-readable instructions is configured to.  Paragraph [0040] describes memory/data storage devices 150 and a processor 11s0.
The rest of claim 15 is substantially similar to claim 1 and is rejected on the same grounds.
Claim 20 is substantially similar to claim 15 and is rejected on the same grounds.
Regarding Claim 4:
Ha discloses:
The method according to claim 1, wherein the calculating the center-of-mass control information comprises: utilizing quadratic programming (QP) optimization to calculate the center-of-mass control information.  Paragraph [0056] describe Sequential Quadratic Programming (SQP) to provide motions of all the key parts of the robot.
Regarding Claim 5:
Ha discloses:
The method according to claim 1, wherein the calculating the center-of-mass control information comprises: utilizing interior point optimizer (IPOPT) optimization to calculate the center-of-mass control information.  Paragraph [0097] describes a interior-point method was used to minimize the iterations to converge and decrease the vibrations from the input sequence.
Regarding Claim 11:
Rinke teaches:
The method according to claim 1, wherein the method further comprises: performing kinetics analysis on a particle model to obtain a kinetics equation, the particle model being obtained by simplification based on a robot body; and analyzing the kinetics equation and constructing the objective function based on an objective of making the center-of-mass motion trajectory of the robot consistent with the center-of-mass reference trajectory.  Paragraph [0147] and figure 17 illustrates data flow during learning of a robot controller.  The neural network performs learning algorithms using unfiltered control parameter 1720.  Kinematics 1730 provides target data over time and outputs a simulation state of the robotic system over time.
Claim 18 is substantially similar to claim 11 and is rejected on the same grounds.
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and Huang and further in view of Chen et al. (US Pub No: 2021/0178587 A1, hereinafter Chen).
Regarding Claim 6:
Ha, Rinke and Huang teach the above limitations in claim 1.  Ha, Rinke and Huang do not teach an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.
Chen teaches:
The method according to claim 1, wherein the obtaining the desired center-of-mass motion data of the robot based on the center-of-mass reference trajectory comprises: obtaining actual center-of-mass motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference center-of-mass motion data of the robot according to the center-of-mass reference trajectory.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining a center-of-mass motion data adjustment value according to a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
and obtaining the desired center-of-mass motion data according to the center-of-mass motion data adjustment value and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke and Huang to incorporate the teachings of Chen to show an actual COM motion data of the robot, a reference COM motion data, obtaining a COM motion data adjustment according to a difference between the actual and the reference COM data and obtaining the desired COM data according to the COM adjustment value.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 17 is substantially similar to claim 6 and is rejected on the same grounds.
Claim(s) 7 – 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke, Huang and Chen and further in view of Oh et al. (US Pub No: 2015/0367518 A1, hereinafter Oh).
Regarding Claim 7:
Chen teaches:
The method according to claim 6, wherein the obtaining the center-of-mass motion data adjustment value according to the difference between the actual center-of-mass motion data and the reference center-of-mass motion data comprises: calculating a difference between the actual center-of-mass motion data and the reference center-of-mass motion data.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
Ha, Rinke, Huang and Chen do not teach a gain matrix associated with the center of mass of the robot.
Oh teaches
and obtaining the center-of-mass motion data adjustment value according to a gain matrix and the difference, the gain matrix corresponding to the motion data associated with the center of mass of the robot.  Paragraph [0071] describes a proportional gain of the COM position unit that represents a safety margin from an asymptotic value.  Paragraph [0102] describes that a proportional-differential controller changes the torque by using a COM Jacobian transposed matrix.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke, Huang and Chen to incorporate the teachings of Oh to show a gain matrix associated with the center of mass of the robot.  One would have been motivated to do so in order to tune a parameter and to get good approximations of complicated calculations.
Regarding Claim 8:
Rinke teaches:
The method according to claim 6, wherein the center-of-mass motion data adjustment value comprises a center-of-mass acceleration adjustment value and a center-of-mass angular acceleration adjustment value.  Paragraph [0066] describes a general inertial acceleration and angular acceleration.
Claim(s) 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ha in view of Rinke and Huang and further in view of Chen.
Regarding Claim 12:
Ha, Rinke and Huang teach the above limitations in claim 1.  Ha, Rinke and Huang do not teach intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.
Chen teaches:
The method according to claim 1, wherein the generating the joint control information according to the center-of-mass control information and the structure matrix of the robot comprises: generating intermediate joint control information according to the center-of-mass control information and the structure matrix corresponding to the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining actual joint motion data of the robot.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.
obtaining reference joint motion data of the robot; and adjusting the intermediate joint control information according to the actual joint motion data and the reference joint motion data to obtain the joint control information.  Paragraph [0030] describes an actual position of the COM of the body and the desire position of the COM of the body.  Paragraph [0032] describes calculating a magnitude of change of the COM of the body in both the x and y direction.  Paragraph [0041] describes that the joint angles are adjusted in real time.
Therefore, it would have been prima facie obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have modified Ha, Rinke and Huang to incorporate the teachings of Chen to show an intermediate joint control information, obtaining actual joint motion data and adjusting the intermediate joint control information according the actual joint motion data and the reference joint motion data.  One would have been motivated to do so in order to control the robot according to the desired joint angles (Abstract of Chen).
Claim 19 is substantially similar to claim 12 and is rejected on the same grounds.
Allowable Subject Matter
Claims 3, 9 - 10 and 13 – 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamane (US Pub No: 2016/0236349 A1): A control method, and a robot controller implementing the method, is provided that adapts human motions to floating-base humanoid robots with time warping techniques. The method of modifying a set of reference motions modifies the timeline of a reference motion so as to speed up or slow down one or more of the motions or motion segments. Through the use of time warping, the velocity and acceleration profiles of the motion are changed to turn an infeasible motion into a feasible one. The optimal time warping is obtained through a generalized motion feasibility index that quantifies the feasibility of a motion considering the friction constraint as well as the center-of-pressure (CoP) constraint. Due to the use of the motion feasibility index, the proposed motion adaptation method taught herein can be applied to motions on arbitrary terrains or with any number of links in contact with the environment.
LaValley (US Pub No: 2020/0369333 A1): A robot actor, or character mobility hardware platform, adapted to unleash or provide a wide variety of characters in the physical world. The robot actor enables the often screen-constrained characters to become life-like, interactive participants with nearby people in ways not presently achievable. The robot actor is an untethered, free-roaming robot that is has two (or more) legs, is adapted for high dexterity, is controlled and designed to be self-balancing, and, due to this combination of characteristics, the robot can provide characters with an illusion of life and, in many cases, in correct proportion and scale. The hardware and software of the robot actor will become a new generation of animatronic figures by providing a hardware platform capable of continuously evolving to become more capable through advances in controls and artificial intelligence (AI).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

CITED REFERENCE SUMMARIES: Ref US20180107175A1: Claims:
A system for automating design of a robot, comprising: a motion optimization module, run by a processor, processing a rough design of a robot including a number of limbs for the robot and further processing a user-specified task for the robot to generate an optimized motion for the robot to perform the user-specified task; and a link length optimization module, run by a processor, processing the optimized motion to generate a set of link lengths for each of the limbs of the robot.
a motion optimization module, run by a processor, processing a rough design of a robot including a number of limbs for the robot and further processing a user-specified task for the robot to generate an optimized motion for the robot to perform the user-specified task; and
a link length optimization module, run by a processor, processing the optimized motion to generate a set of link lengths for each of the limbs of the robot.
The system of claim 1 , wherein the rough design further comprises an approximate body size and a number of legs.
The system of claim 2 , wherein the user-specified task includes contact positions for the robot and wherein the optimized motion is generated to minimize contact forces for the robot while performing the user-specified task based on the contact positions.
The system of claim 1 , wherein the optimized motion includes a trajectory of the center of mass (COM) of the robot while performing the user-specified task.
The system of claim 1 , wherein the set of link lengths are determined by the link length optimization module as optimal lengths of links in each of the limbs of the robot that minimize a sum of joint torques for the user-specified task.
The system of claim 5 , wherein the link length optimization module solves joint positions, joint velocities, joint acceleration, fullbody contact forces, and joint torques.
The system of claim 1 , further comprising a morphology optimization module, run by a processor, processing as input the rough design of the robot, the user-specified task, the optimized motion, and a library of modular components for fabricating the robot to generate an optimized morphology for processing by the link length optimization module.
The system of claim 7 , wherein the optimized morphology defines a number and type of link for each of the limbs, a location and orientation for each joint in each of the limbs, and a number and location of servos for performing the user-specified task.
The system of claim 7 , wherein the optimized morphology is generated with an algorithm configured to solve a shortest path finding problem in a directed graph with nodes in the directed graph each corresponding to a different design of the robot.
The system of claim 9 , wherein the algorithm comprises a best-first search algorithm including a heuristic function.
The system of claim 10 , wherein the heuristic function is a tip-based heuristic function.
A system for designing a robotic device, comprising: a motion optimization module, run by a processor, processing a rough design of a robotic device and a user-specified task for the robotic device to generate an optimized motion for the robotic device; and a morphology optimization module, run by a processor, processing as input the rough design of the robotic device, the user-specified task, the optimized motion, and a library of modular components for fabricating the robotic device to generate an optimized morphology for the robotic device.
a motion optimization module, run by a processor, processing a rough design of a robotic device and a user-specified task for the robotic device to generate an optimized motion for the robotic device; and
a morphology optimization module, run by a processor, processing as input the rough design of the robotic device, the user-specified task, the optimized motion, and a library of modular components for fabricating the robotic device to generate an optimized morphology for the robotic device.
The system of claim 12 , wherein the robotic device is a manipulator, a modular robot, or a legged robot.
The system of claim 13 , wherein the robotic device is a legged robot and wherein the optimized morphology defines a number and type of link for each leg of the legged robot and a location and orientation for each joint in each of the legs.
The system of claim 14 , further including a link length optimization module, run by a processor, processing the optimized motion and the optimized morphology to generate a set of link lengths for each of the legs of the robotic device.
The system of claim 15 , wherein the set of link lengths are determined by the link length optimization module as optimal lengths of links in each of the legs of the robotic device that minimize a sum of joint torques for the user-specified task.
The system of claim 12 , wherein the optimized morphology is generated with an algorithm configured to solve a shortest path finding problem in a directed graph with nodes in the directed graph each corresponding to a different design of the robot.
The system of claim 17 , wherein the algorithm comprises a best-first search algorithm including a tip-based heuristic function.
A method for automated robot design, comprising: receiving a task definition for a robot including a trajectory for a COM or trajectories for end effectors; optimizing the trajectory of the COM or the trajectories for the end effectors to limit contact forces to provide an optimized motion for the robot; and generating a morphology for the robot by selecting a subset of components from a library of modular components to perform the optimized motion, wherein the generating the morphology includes solving a shortest path finding problem in a directed graph with nodes in the directed graph each corresponding to a different design of the robot formed using the library of modular components.
receiving a task definition for a robot including a trajectory for a COM or trajectories for end effectors;
optimizing the trajectory of the COM or the trajectories for the end effectors to limit contact forces to provide an optimized motion for the robot; and
generating a morphology for the robot by selecting a subset of components from a library of modular components to perform the optimized motion,
wherein the generating the morphology includes solving a shortest path finding problem in a directed graph with nodes in the directed graph each corresponding to a different design of the robot formed using the library of modular components.
The method of claim 19 , wherein the processing to generate the morphology includes performing a best-first search.
The method of claim 20 , wherein the best-first search is an A* search with a tip-based heuristic.
The method of claim 19 , wherein the robot is a legged robot, wherein the morphology defines a number and type of link for each leg of the legged robot, and wherein the method further comprises defining a length of each of the links, whereby a sum of joint torques is minimized when the robot follows the trajectory of the COM or the trajectories for the end effectors.
The method of claim 19 , wherein the robot is a manipulator, a modular robot, or a legged robot.
Ref US20220184802A1: Claims:
A robotic system configured for real-time vibration-suppressing control, comprising: a plurality of components connected by mechanical joints, wherein a subset of the components is modeled as rigid bodies and a subset is modeled as flexible bodies and wherein a subset or all of the mechanical joints is modeled as flexible joints; memory storing input comprising unfiltered control parameters specifying motion of the components over a time period; a plurality of actuators operable in response to control signals to impart the motion of the components over the time period; and a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations, wherein the optimizer is trained via machine learning based on a set of training data prior to generating the retargeted motion, and wherein the controller generates the control signals for operating the actuators based on the filtered control parameters.
a plurality of components connected by mechanical joints, wherein a subset of the components is modeled as rigid bodies and a subset is modeled as flexible bodies and wherein a subset or all of the mechanical joints is modeled as flexible joints;
memory storing input comprising unfiltered control parameters specifying motion of the components over a time period;
a plurality of actuators operable in response to control signals to impart the motion of the components over the time period; and
a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations, wherein the optimizer is trained via machine learning based on a set of training data prior to generating the retargeted motion, and wherein the controller generates the control signals for operating the actuators based on the filtered control parameters.
a processor communicatively linked to the memory;
an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations, wherein the optimizer is trained via machine learning based on a set of training data prior to generating the retargeted motion, and wherein the controller generates the control signals for operating the actuators based on the filtered control parameters.
The system of claim 1 , further comprising a plurality of sensors monitoring operations of the robotic system and wherein the training data includes sensory feedback from the plurality of sensors.
The system of claim 1 , wherein the optimizer comprises a neural network configured for the machine learning and to take as input the unfiltered control parameters and output the filtered control parameters.
The system of claim 3 , wherein the neural network includes a final node comprising a differentiable simulator, wherein the differentiable simulator performs a dynamic simulation of the robotic system performing the retargeted motions with the components, and wherein the dynamic simulation predicts the vibrations for the robotic system in performing the retargeted motion.
The system of claim 4 , wherein the differentiable simulator outputs a time varying state of the robotic system and wherein the controller further comprises a loss function that measures, based on the time varying state, effectiveness of the learned controller by comparing differences in absolute positions or orientations of the components to their target positions or orientations.
The system of claim 1 , wherein the machine learning is performed prior to operations of the controller to generate the control signals and comprises comparisons of target states defined by a plurality of input animations defining motions for the components via operations of the actuators and states determined by a differentiable simulator, wherein the differentiable simulator performs a simulation of the robotic system performing the retargeted motions with the components, and wherein the dynamic simulation predicts the vibrations for the robotic system in performing the retargeted motion.
The system of claim 1 , wherein the optimizer comprises a neural network configured for the machine learning and wherein the neural network comprises layers of at least one of 1D convolutional networks and 1D recurrent neural networks.
The system of claim 1 , wherein the portion of the vibrations that is suppressed comprises low-frequency, large-amplitude vibrations of one or more of the components of the robot, wherein the unfiltered control parameters include a set of motor trajectories, and wherein the optimizer modifies the set of motor trajectories to generate the retargeted motion for the components.
A robotic system configured for real-time vibration-suppressing control, comprising: a plurality of components; memory storing input comprising unfiltered control parameters specifying motion of the components over a time period; a plurality of motors operable to impart the motion of the components; a plurality of sensors monitoring operations of the robotic system; and a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates, based on the unfiltered control parameters, filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components, wherein the optimizer is trained, prior to generating the retargeted motion, via machine learning based on a set of training data, wherein the controller operates the actuators based on the filtered control parameters, and wherein the training data includes sensory feedback from the plurality of sensors
a plurality of components;
a plurality of motors operable to impart the motion of the components;
a plurality of sensors monitoring operations of the robotic system; and
a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates, based on the unfiltered control parameters, filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components,
an optimizer provided by the processor running software, wherein the optimizer generates, based on the unfiltered control parameters, filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components,
wherein the optimizer is trained, prior to generating the retargeted motion, via machine learning based on a set of training data,
wherein the controller operates the actuators based on the filtered control parameters, and
wherein the training data includes sensory feedback from the plurality of sensors
The system of claim 9 , wherein the optimizer comprises a neural network configured for the machine learning and to take as input the unfiltered control parameters and output the filtered control parameters.
The system of claim 10 , wherein the neural network includes a final node comprising a differentiable simulator, wherein the differentiable simulator performs a dynamic simulation of the robotic system performing the retargeted motions with the components, and wherein the dynamic simulation predicts vibrations for the robotic system in performing the retargeted motion.
The system of claim 11 , wherein the differentiable simulator outputs a time varying state of the robotic system and wherein the controller further comprises a loss function that measures, based on the time varying state, effectiveness of the learned controller by comparing differences in absolute positions or orientations of the components to their target positions or orientations.
The system of claim 9 , wherein the machine learning comprises comparisons of target states defined by a plurality of input animations defining motions for the first and second sets of components via operations of the actuators and states determined by a differentiable simulator, wherein the differentiable simulator performs a simulation of the robotic system performing the retargeted motions with the first and second sets of the components, and wherein the dynamic simulation predicts vibrations for the robotic system in performing the retargeted motion.
The system of claim 9 , wherein the optimizer comprises a neural network configured for the machine learning and wherein the neural network comprises layers of at least one of 1D convolutional networks and 1D recurrent neural networks.
The system of claim 9 , wherein the vibrations that are suppressed comprises low-frequency, large-amplitude vibrations of one or more of the components of the robot, wherein the unfiltered control parameters include a set of motor trajectories, and wherein the optimizer modifies the set of motor trajectories to generate the retargeted motion for the components.
A robotic system configured for real-time vibration-suppressing control, comprising: a plurality of components; memory storing input comprising unfiltered control parameters specifying motion of the components over a time period; and a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components, wherein the optimizer comprises a neural network that is trained via machine learning based on a set of training data to suppress the vibrations while concurrently minimizing differences between the filtered control parameters and simulated control parameters in the set of training data.
memory storing input comprising unfiltered control parameters specifying motion of the components over a time period; and
a controller comprising: a processor communicatively linked to the memory; an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components,
an optimizer provided by the processor running software, wherein the optimizer generates filtered control parameters defining a retargeted motion for the components by adjusting the defined motion to suppress vibrations of one or more of the components,
wherein the optimizer comprises a neural network that is trained via machine learning based on a set of training data to suppress the vibrations while concurrently minimizing differences between the filtered control parameters and simulated control parameters in the set of training data.
The system of claim 16 , further comprising a plurality of sensors monitoring operations of the robotic system and wherein the set of the training data includes sensory feedback from the plurality of sensors.
The system of claim 16 , wherein the neural network includes a final node comprising a differentiable simulator, wherein the differentiable simulator performs a dynamic simulation of the robotic system performing the retargeted motions with the first and second sets of the components, and wherein the dynamic simulation predicts vibrations for the robotic system in performing the retargeted motion.
The system of claim 19 , wherein the differentiable simulator outputs a time varying state of the robotic system and wherein the controller further comprises a loss function that measures, based on the time varying state, effectiveness of the learned controller by comparing differences in absolute positions or orientations of the components to their target positions or orientations.
The system of claim 16 , wherein the neural network comprises layers of at least one of 1D convolutional networks and 1D recurrent neural networks and wherein the portion of the vibrations that is suppressed comprises low-frequency, large-amplitude vibrations of one or more of the components of the robot.
Ref US20210178587A1: Claims:
A computer-implemented method executed by one or more processors of a robot, the method comprising: obtaining force information associated with a left foot 
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