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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/14/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 16, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-8, 10, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitta et al. (US 20200047331 A1, hereinafter Chitta) in view of Fan (US 20220072707 A1).
Regarding claim 1, Chitta discloses a method of determining a grasp strategy to grasp an object with a suction-based gripper of a robotic device (at least as in paragraph 0023, wherein the environment includes “a robot 100 with a control system 110, a visual sensor 120, and an arm 130 with a gripper 200 . . . control system 110 is configured to control the robot 100 by, for example, operation of the gripper 200 to manipulate boxes 22 in the environment”; at least as in paragraph 0043, “a method 700 of using a zoned gripper 200 to grasp and move a target candidate box 24T”), the method comprising:
generating, by at least one computing device, a set of grasp candidates to grasp a target object, wherein each of the grasp candidates includes information about a gripper placement relative to the target object (see Fig. 3A-3J; at least as in paragraph 0032, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked. Multiple grasp poses P.sub.G may be computed for each box 24”; at least as in paragraph 0034 & 0035, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; therefore, the control system generates a plurality or set of candidate grasp poses to position the suction cup gripper over the top surface of each target candidate box);
determining, by the at least one computing device, for each of the grasp candidates in the set, a grasp quality, wherein the grasp quality is determined based on the modeling for the respective grasp candidate (at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0036, wherein “the computing device 40 and/or control system 110 is configured to check whether the part-presence sensor 220 overlaps with the box 24T by a sufficient margin”; at least as in paragraph 0034-0035, wherein the control system takes into consideration “potential obstacle[s] for the gripper 200 when maneuvering boxes 22 inside the container 30,” such as the walls and “the lip 32 of the container 30,” and utilizes the plurality of grasp poses “to avoid a collision or disturbance with the container 30 during picking the target candidate box 24T”; at least as in paragraph 0037-0039, wherein the control system “compute[s] the overlap of each gripper zone Z with neighboring boxes 26 that are not candidates for picking”; therefore, at least as in paragraph 0005 and 0008, the system includes “determining that the grasp pose includes minimum coverage of the target candidate box where the minimum coverage corresponds to an area providing suction force sufficient to lift the target candidate box”);
selecting, by the at least one computing device based at least in part on the determined grasp qualities, one of the grasp candidates (at least as in paragraph 0043, wherein the control system “determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”); and
controlling, by the at least one computing device, the robotic device to attempt to grasp the target object using the selected grasp candidate (at least as in paragraph 0043, wherein “the method 700 executes the grasp pose P.sub.G to lift the target candidate box 24T by the gripper 200”).
Chitta does not explicitly disclose “modeling, by the at least one computing device, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based-gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper.”
However, Fan, in the same field of endeavor of robot grasp planning through determining the grasp quality of proposed grasps, specifically teaches “modeling, by the at least one computing device, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based-gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper” (at least as in paragraph 0019, “Many different styles of grippers may be included in the gripper database 114—including two- and three-finger articulated grippers, parallel-jaw grippers, full-human-hand style grippers, underconstrained actuation grippers, suction cup style grippers (single or multiple cups), etc.”; at least as in paragraph 0063, “The optimization framework works on suction grippers, conventional finger-type grippers, customized grippers, multi-fingered hands and soft grippers with minor adaptation.”; at least as in paragraph 0030, “At box 230, the grasp searching problem is modeled as an optimization, and one iteration is computed. To compute stable grasps, surface contacts and rigorous mathematic quality are adopted in the modeling”; at least as in paragraph 0031, “The optimization formulation includes an objective function (Eq. 1a) defined to maximize grasp quality Q… The grasp quality Q may be defined in any suitable manner, and is computed from the force contributions of all of the contact points relative to object properties such as mass and center of gravity”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Fan's teaching of grasp control system configured to optimize the grasping method through modeling, since Fan teaches wherein the modeling allows each grasp to be evaluated for its robustness to variables such as gripper collisions, unknown friction values, stability under pose errors and stability with gripper force limits thus providing more stable grasps through computationally efficient means.
Regarding claim 2, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, wherein generating a grasp candidate in the set of grasp candidates comprises:
selecting a gripper placement relative to the target object (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked”);
determining whether the selected gripper placement is possible without colliding with one or more other objects in an environment of the robotic device (at least as in paragraph 0034, wherein “By having a plurality of grasp poses P.sub.G and/or offset grasp poses P.sub.Goff, the gripper 200 may have options to avoid a collision or disturbance with the container 30 during picking the target candidate box 24T”; at least as in paragraph 0035, wherein “The computation of a viable grasp pose P.sub.G also accounts for the presence of the walls 30w of the container 30 . . . other boxes 22 . . . the lip 32 of the container 30,” and other potential obstacles); and
generating the grasp candidate in the set of grasp candidates when it is determined that the selected gripper placement is possible without colliding with one or more other objects in the environment of the robotic device (at least as in paragraph 0043, wherein “the method 700 determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”).
Regarding claim 3, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, further comprising:
determining that at least one object other than the target object is capable of being grasped at a same time as the target object (at least as in paragraph 0037, wherein “the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked [and] with neighboring boxes 26”); and
determining the information about the gripper placement for the grasp candidate to grasp both the target object and the at least one object other than the target object at the same time (at least as in paragraph 0026, wherein “When transporting boxes 22 from a single-SKU pallet 20, all zones Z of a zoned gripper 200 may be activated (e.g., turned on) to permit the picking multiple boxes that overlap with the gripper 200”; 744at least as in paragraph 0043, wherein “For each zone Z of the plurality of zones Z, at operation 708, the method 700 determines an overlap with a respective zone Z with one or more neighboring boxes 26 to the set of candidate boxes 24. Here, the neighboring boxes 26 are identified by the image from the visual sensor 120”).
Regarding claim 4, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, wherein generating a grasp candidate in the set of grasp candidates comprises:
determining, based on the information about the gripper placement, a set of suction cups of the suction-based gripper to activate (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked”; at least as in paragraph 0037, wherein “the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”); and
associating with the grasp candidate, information about the set of suction cups of the suction-based gripper to activate (at least as in paragraph 0037, wherein “for each grasp pose P.sub.G, the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”; at least as in paragraph 0038 and Figs. 4A-4C, wherein the activation of the appropriate zone matches the size requirement of each target candidate box),
wherein determining the grasp quality for a respective grasp candidate based on the modeling is further based, at least in part, on the information about the set of suction cups of the suction-based gripper to activate (at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0043, wherein “the method 700 divides a grip area of the gripper 200 into a plurality of zones Z based on the minimum box size” and “For each zone Z of the plurality of zones Z, at operation 708, the method 700 determines an overlap with a respective zone Z with one or more neighboring boxes 26 to the set of candidate boxes 24”).
Regarding claim 6, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 4, wherein determining the set of suction cups of the suction-based gripper to activate comprises including, in the set of suction cups, all suction cups in the suction-based gripper completely overlapping a surface of the target object (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked”; at least as in paragraph 0037, wherein “the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”; at least as in paragraph 0037, wherein “for each grasp pose P.sub.G, the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”; at least as in paragraph 0038 and Figs. 4A-4C, wherein the activation of the appropriate zone matches the size requirement of each target candidate box; at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0043, wherein “the method 700 divides a grip area of the gripper 200 into a plurality of zones Z based on the minimum box size” and “For each zone Z of the plurality of zones Z, at operation 708, the method 700 determines an overlap with a respective zone Z with one or more neighboring boxes 26 to the set of candidate boxes 24”).
Regarding claim 7, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, wherein the set of grasp candidates includes a first grasp candidate having a first offset relative to the target object and a second grasp candidate having a second offset relative to the target object, wherein the second offset is different from the first offset (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24 . . . Multiple grasp poses P.sub.G may be computed for each box 24 . . . the control system 110 and/or computing device 40, generate an offset grasp pose P.sub.Goff (e.g., based off a grasp pose P.sub.G) as an alternative grasp pose P.sub.G to lift the target candidate box 24, 24T. For instance, FIG. 3C depicts two offset grasp poses P.sub.Goff,1-2. In FIGS. 3C and 3D, neither grasp pose P.sub.G fully overlaps the target candidate box 24, 24T”; at least as in paragraph 0034, wherein “the control system 110 and/or computing device 40 is configured to generate a plurality of grasp poses P.sub.G (e.g., including offset grasp poses P.sub.Goff). For instance, generating the plurality of grasp poses P.sub.G may include generating an offset grasp pose P.sub.Goff for each candidate grasp pose P.sub.G (e.g., FIG. 3E shows three grasp poses P.sub.Goff1-3 with three candidate grasp pose P.sub.G1-3)”; at least as in paragraph 0034, wherein “Grasp poses P.sub.G may also be offset in orientation from the orientation of the box 22 and the orientation of the container 30 (e.g., as shown in FIG. 3E)”).
Regarding claim 8, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, wherein the set of grasp candidates includes a first grasp candidate having a first orientation relative to the target object and a second grasp candidate having a second orientation relative to the target object, wherein the second orientation is different from the first orientation (at least as in paragraph 0034, wherein “the control system 110 and/or computing device 40 is configured to generate a plurality of grasp poses P.sub.G (e.g., including offset grasp poses P.sub.Goff)”; at least as in paragraph 0035, wherein “Grasp poses P.sub.G may also be offset in orientation from the orientation of the box 22 and the orientation of the container 30 (e.g., as shown in FIG. 3E)”).
Regarding claim 10, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, further comprising:
determining, by the at least one computing device, whether the selected grasp candidate is feasible (at least as in paragraph 0035, wherein “computation of a viable grasp pose P.sub.G also accounts for the presence of the walls 30w of the container 30”; at least as in paragraph 0037, “systems for the robot 100 (the control system 110 and/or computing device 40) compute the overlap of each gripper zone Z with neighboring boxes 26 that are not candidates for picking”); and
performing, by the at least one computing device, at least one action when it is determined that the selected grasp candidate is not feasible (at least as in paragraph 0039, wherein “When there is overlap between the zones Z and neighboring boxes 26 that are not candidates for picking, any zone Z that overlaps a neighboring box 26 is not initially activated when picking an individual box 22”).
Regarding claim 15, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, further comprising:
receiving, by the at least one computing device, a selection of the target object to grasp by the gripper of the robotic device (at least as in paragraph 0043, wherein “At operation 702, the method 700 receives, at system (e.g., the control system 110 or the computing device 40) of a robot 100, a minimum box size for a plurality of boxes 22 varying in size where the plurality of boxes 22 are located in a walled container 30 . . . the method 700 determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”; therefore, a target candidate box is determined ).
Regarding claim 16, Chitta discloses a robotic device (Fig. 1, robot 100), comprising:
a robotic arm having disposed thereon, a suction-based gripper configured to grasp a target object (at least as in paragraph 0023, wherein “arm 130 with a gripper 200 . . . The control system 110 is configured to control the robot 100 by, for example, operation of the gripper 200 to manipulate boxes 22 in the environment”; at least as in paragraph 0026, wherein “zoned vacuum grippers may include multiple suction cup zones Z”); and
at least one computing device (Fig. 1, control system 110) configured to:
generate a set of grasp candidates to grasp the target object, wherein each of the grasp candidates includes information about a gripper placement relative to the target object (see Fig. 3A-3J; at least as in paragraph 0032, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked. Multiple grasp poses P.sub.G may be computed for each box 24”; at least as in paragraph 0034 & 0035, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; therefore, the control system generates a plurality or set of candidate grasp poses to position the suction cup gripper over the top surface of each target candidate box);
determine, for each of the grasp candidates in the set, a grasp quality, wherein the grasp quality is determined based on the model for the respective grasp candidate (at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0036, wherein “the computing device 40 and/or control system 110 is configured to check whether the part-presence sensor 220 overlaps with the box 24T by a sufficient margin”; at least as in paragraph 0034-0035, wherein the control system takes into consideration “potential obstacle[s] for the gripper 200 when maneuvering boxes 22 inside the container 30,” such as the walls and “the lip 32 of the container 30,” and utilizes the plurality of grasp poses “to avoid a collision or disturbance with the container 30 during picking the target candidate box 24T”; at least as in paragraph 0037-0039, wherein the control system “compute[s] the overlap of each gripper zone Z with neighboring boxes 26 that are not candidates for picking”; therefore, at least as in paragraph 0005 and 0008, the system includes “determining that the grasp pose includes minimum coverage of the target candidate box where the minimum coverage corresponds to an area providing suction force sufficient to lift the target candidate box”);
select based, at least in part, on the determined grasp qualities, one of the grasp candidates (at least as in paragraph 0043, wherein the control system “determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”); and
control the arm of the robotic device to attempt to grasp the target object using the selected grasp candidate (at least as in paragraph 0043, wherein “the method 700 executes the grasp pose P.sub.G to lift the target candidate box 24T by the gripper 200”).
Chitta does not explicitly disclose “model, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper.”
However, Fan, in the same field of endeavor of robot grasp planning through determining the grasp quality of proposed grasps, specifically teaches “model, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper” (at least as in paragraph 0019, “Many different styles of grippers may be included in the gripper database 114—including two- and three-finger articulated grippers, parallel-jaw grippers, full-human-hand style grippers, underconstrained actuation grippers, suction cup style grippers (single or multiple cups), etc.”; at least as in paragraph 0063, “The optimization framework works on suction grippers, conventional finger-type grippers, customized grippers, multi-fingered hands and soft grippers with minor adaptation.”; at least as in paragraph 0030, “At box 230, the grasp searching problem is modeled as an optimization, and one iteration is computed. To compute stable grasps, surface contacts and rigorous mathematic quality are adopted in the modeling”; at least as in paragraph 0031, “The optimization formulation includes an objective function (Eq. 1a) defined to maximize grasp quality Q… The grasp quality Q may be defined in any suitable manner, and is computed from the force contributions of all of the contact points relative to object properties such as mass and center of gravity”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Fan's teaching of grasp control system configured to optimize the grasping method through modeling, since Fan teaches wherein the modeling allows each grasp to be evaluated for its robustness to variables such as gripper collisions, unknown friction values, stability under pose errors and stability with gripper force limits thus providing more stable grasps through computationally efficient means.
Regarding claim 17, in view of the above combination of Chitta and Fan, Chitta further discloses the robotic device of claim 16, wherein generating a grasp candidate in the set of grasp candidates comprises:
selecting a gripper placement of the suction-based gripper relative to the target object (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked”);
determining whether the selected gripper placement is possible without colliding with one or more other objects in an environment of the robotic device (at least as in paragraph 0034, wherein “By having a plurality of grasp poses P.sub.G and/or offset grasp poses P.sub.Goff, the gripper 200 may have options to avoid a collision or disturbance with the container 30 during picking the target candidate box 24T”; at least as in paragraph 0035, wherein “The computation of a viable grasp pose P.sub.G also accounts for the presence of the walls 30w of the container 30 . . . other boxes 22 . . . the lip 32 of the container 30,” and other potential obstacles); and
generating the grasp candidate in the set of grasp candidates when it is determined that the selected gripper placement is possible without colliding with one or more other objects in the environment of the robotic device (at least as in paragraph 0043, wherein “the method 700 determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”).
Regarding claim 18, in view of the above combination of Chitta and Fan, Chitta further discloses the robotic device of claim 16, wherein the suction-based gripper includes one or more suction cups, and wherein the at least one computing device is further configured to:
determine, based on the information about the gripper placement, a set of suction cups of the one or more suction cups to activate (at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked”; at least as in paragraph 0037, wherein “the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”); and
associate with the grasp candidate, information about the set of suction cups of the one or more suction cups to activate (at least as in paragraph 0037, wherein “for each grasp pose P.sub.G, the control system 110 and/or computing device 40 may compute the overlap of various gripper zones Z with the box 24, 24T to be picked”; at least as in paragraph 0038 and Figs. 4A-4C, wherein the activation of the appropriate zone matches the size requirement of each target candidate box).
Regarding claim 20, Chitta discloses a non-transitory computer readable medium encoded with a plurality of instructions that, when executed by at least one computing device, perform a method (at least as in paragraph 0024, wherein “the computer program instructions on which various implementations are based . . . may be stored in any type of non-transitory computer-readable storage media or memory device(s), and may be executed according to a variety of computing models), the method comprising:
generating a set of grasp candidates to grasp a target object using a suction-based gripper, wherein each of the grasp candidates includes information about a gripper placement relative to the target object (see Fig. 3A-3J; at least as in paragraph 0032, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; at least as in paragraph 0033, wherein “the control system 110 and/or computing device 40 may compute a set of grasp poses P.sub.G for each candidate box 24. A grasp pose P.sub.G is a position of the gripper 200 where a subset or all of the suction cups 210 on the gripper 200 overlap (partly or fully) the top surface of the object being picked. Multiple grasp poses P.sub.G may be computed for each box 24”; at least as in paragraph 0034 & 0035, wherein “the control system 110 uses data obtained from the visual sensor 120 mounted above the container 30 (e.g., overhead and/or on the robot 100 itself) to locate a set of candidate boxes 24, 24.sub.set (e.g., shown in FIG. 1) on the top of the stack of boxes 22”; therefore, the control system generates a plurality or set of candidate grasp poses to position the suction cup gripper over the top surface of each target candidate box);
determining for each of the grasp candidates in the set, a grasp quality, wherein the grasp quality is determined based on the modeling for the respective grasp candidate (at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0036, wherein “the computing device 40 and/or control system 110 is configured to check whether the part-presence sensor 220 overlaps with the box 24T by a sufficient margin”; at least as in paragraph 0034-0035, wherein the control system takes into consideration “potential obstacle[s] for the gripper 200 when maneuvering boxes 22 inside the container 30,” such as the walls and “the lip 32 of the container 30,” and utilizes the plurality of grasp poses “to avoid a collision or disturbance with the container 30 during picking the target candidate box 24T”; at least as in paragraph 0037-0039, wherein the control system “compute[s] the overlap of each gripper zone Z with neighboring boxes 26 that are not candidates for picking”; therefore, at least as in paragraph 0005 and 0008, the system includes “determining that the grasp pose includes minimum coverage of the target candidate box where the minimum coverage corresponds to an area providing suction force sufficient to lift the target candidate box”);
selecting based at least in part on the determined grasp qualities, one of the grasp candidates (at least as in paragraph 0043, wherein the control system “determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”); and
controlling a robotic device to attempt to grasp the target object using the selected grasp candidate (at least as in paragraph 0043, wherein “the method 700 executes the grasp pose P.sub.G to lift the target candidate box 24T by the gripper 200”).
Chitta does not explicitly disclose “modeling, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper.”
However, Fan, in the same field of endeavor of robot grasp planning through determining the grasp quality of proposed grasps, specifically teaches “modeling, for each of the grasp candidates in the set, one or more forces between the target object and the suction-based gripper located at the gripper placement for the respective grasp candidate, wherein the one or more forces include a suction force between the target object and the suction-based gripper” (at least as in paragraph 0019, “Many different styles of grippers may be included in the gripper database 114—including two- and three-finger articulated grippers, parallel-jaw grippers, full-human-hand style grippers, underconstrained actuation grippers, suction cup style grippers (single or multiple cups), etc.”; at least as in paragraph 0063, “The optimization framework works on suction grippers, conventional finger-type grippers, customized grippers, multi-fingered hands and soft grippers with minor adaptation.”; at least as in paragraph 0030, “At box 230, the grasp searching problem is modeled as an optimization, and one iteration is computed. To compute stable grasps, surface contacts and rigorous mathematic quality are adopted in the modeling”; at least as in paragraph 0031, “The optimization formulation includes an objective function (Eq. 1a) defined to maximize grasp quality Q… The grasp quality Q may be defined in any suitable manner, and is computed from the force contributions of all of the contact points relative to object properties such as mass and center of gravity”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Fan's teaching of grasp control system configured to optimize the grasping method through modeling, since Fan teaches wherein the modeling allows each grasp to be evaluated for its robustness to variables such as gripper collisions, unknown friction values, stability under pose errors and stability with gripper force limits thus providing more stable grasps through computationally efficient means.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitta et al. (US 20200047331 A1, hereinafter Chitta) in view of Fan (US 20220072707 A1), and further in view of Zevenbergen et al. (US 9205558 B1, hereinafter Zevenbergen).
Regarding claim 5, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 4, wherein modeling the one or more forces between the target object and the suction-based gripper located at the gripper placement for the respective grasp candidate comprises:
determining the grasp quality for the respective grasp candidate (at least as in paragraph 0037, wherein “every grasp pose P.sub.G may be restricted to have a minimum coverage of the box 24, 24T being picked to ensure that there is sufficient suction force to pick the box 22”; at least as in paragraph 0036, wherein “the computing device 40 and/or control system 110 is configured to check whether the part-presence sensor 220 overlaps with the box 24T by a sufficient margin”; therefore, at least as in paragraph 0005 and 0008, the system includes “determining that the grasp pose includes minimum coverage of the target candidate box where the minimum coverage corresponds to an area providing suction force sufficient to lift the target candidate box”).
Chitta does not specifically disclose “modeling forces between the target object and each suction cup in the set of suction cups of the suction-based gripper to activate; and… based on an aggregate of the modeled forces between the target object and each suction cup in the set of suction cups of the suction-based gripper to activate.”
However, Fan, in the same field of endeavor of robot grasp planning through determining the grasp quality of proposed grasps, specifically teaches “modeling forces between the target object” (at least as in paragraph 0019, “Many different styles of grippers may be included in the gripper database 114—including two- and three-finger articulated grippers, parallel-jaw grippers, full-human-hand style grippers, underconstrained actuation grippers, suction cup style grippers (single or multiple cups), etc.”; at least as in paragraph 0063, “The optimization framework works on suction grippers, conventional finger-type grippers, customized grippers, multi-fingered hands and soft grippers with minor adaptation.”; at least as in paragraph 0030, “At box 230, the grasp searching problem is modeled as an optimization, and one iteration is computed. To compute stable grasps, surface contacts and rigorous mathematic quality are adopted in the modeling”; at least as in paragraph 0031, “The optimization formulation includes an objective function (Eq. 1a) defined to maximize grasp quality Q… The grasp quality Q may be defined in any suitable manner, and is computed from the force contributions of all of the contact points relative to object properties such as mass and center of gravity”).
Zevenbergen, in the same field of endeavor, discloses a system configured to control a suction gripper that includes a plurality of suction cups based on sensor data indicative of a vacuum pressure of the one or more suction cups. Zevenbergen teaches “and each suction cup in the set of suction cups of the suction-based gripper to activate; and… based on an aggregate of the modeled forces between the target object and each suction cup in the set of suction cups of the suction-based gripper to activate” (at least as in col. 8, lines 44-59, wherein “a virtual environment including a model of the objects in 2D and/or 3D may be determined and used to develop a plan or strategy for picking up the boxes . . . the robot may use one or more sensors to scan an environment containing objects [and] capture sensor data about the stack of boxes 220 in order to determine shapes and/or positions of individual boxes”;
at least as in col. 17, lines 15-28, wherein “the sensor data received from the in-line sensors may indicate the current vacuum pressure levels of active suction cups 406, 408, 412, 414, 418, and 420 . . . as bars 476, 478, 482, 484, 488, and 490”; at least as in col. 17, lines 29-47, wherein “the control system of suction gripper 400 may identify one or more of the active suction cups 406, 408, 412, 414, 418, and 420 to deactivate based on the sensor data depicted by bars 476, 478, 482, 484, 488, and 490” as “a solution to an optimization problem may indicate that the overall gripping force of suction gripper 400 may be improved by deactivating suction cups 418 and 420”; at least as in col. 18, lines 44-52, wherein “a particular optimization of the suction gripper resulting in a certain subset of active suction cups may produce a certain amount of gripping force on the object . . . this total gripping force may be estimated using the sensor data from the in-line sensors indicative of the vacuum pressures of individual suction cups [and] may indicate a current maximum gripping capability of the gripper that may be used to determine a trajectory that won't cause the gripper to drop the object”;
additionally, Zevenbergen discloses, at least as in col. 14, lines 32-41, wherein “three-dimensional visual data may be used to determine an estimate of surface quality of object surfaces with respect to suction cup performance”; at least as in col. 14, lines 55-64, wherein “the surface quality metric may be used as part of a grasp search algorithm to find a good grasp placement . . . to estimate probable gripper forces that the gripper might exert . . . this estimation may be used by a control algorithm to abort a grasp or choose a more conservative trajectory to move an object . . . the surface quality metric and corresponding estimates of probable gripper forces may be used as a prior to a vacuum suction cup control algorithm”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Fan's teaching of utilizing a physical simulation box to determine a grasp quality value for each proposed grasp and Zevenbergen 's teaching of generating a virtual environment, determining a surface quality metric in a grasp search algorithm to estimate probable gripper forces by the vacuum suction cups, and determining the vacuum pressure of individual suctions cups and the total gripping force on the object through in-line sensors and pressure data bars, since Fan teaches wherein the optimization model allows each grasp to be evaluated for its robustness to variables such as gripper collisions, unknown friction values, stability under pose errors and stability with gripper force limits thus providing more stable grasps through computationally efficient means and Zevenbergen teaches wherein the virtual environment, the pressure identification, and the gripper force estimation optimize and improve the gripping force and capability of the suction gripper.
Claim(s) 9 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitta et al. (US 20200047331 A1, hereinafter Chitta) in view of Fan (US 20220072707 A1), and further in view of Marchese et al. (US 20200398441 A1, hereinafter Marchese).
Regarding claim 9, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, wherein selecting based, at least in part, on the determined grasp qualities, one of the grasp candidates comprises selecting the grasp candidate in the set of grasp candidates (at least as in paragraph 0043, wherein the control system “determines a grasp pose P.sub.G for a target candidate box 24T of the set of candidate boxes 24 that avoids one or more walls 30w of the walled container 30”).
However, Chitta does not specifically teach “with the highest grasp quality.”
Marchese, in the same field of endeavor, discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contact points using estimated seal quality metrics. Marchese teaches “with the highest grasp quality” (at least as in paragraph 0083, wherein “the robotic control component 315 receiving the estimated seal quality metrics for the candidate contact points from the seal quality estimation component 330 (block 1120). The robotic control component 315 selects a candidate contact point having the greatest estimated seal quality metric and that satisfies one or more predefined criteria (block 1210). For example, the robotic control component 315 could identify a candidate contact point that has a seal quality metric that exceeds a predefined threshold level of seal quality and that is the greatest seal quality metric across all of the considered candidate contact points”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of selecting a candidate contact point having the greatest estimated seal quality metric, since Marchese teaches wherein the use of estimated seal quality metrics for candidate contact points significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item, thus, reducing possible delay of the workflow and improving environment safety.
Regarding claim 11, the above combination of Chitta and Fan discloses the method of claim 10, however, Chitta does not specifically disclose “wherein performing at least one action comprises selecting a different grasp candidate from the set of grasp candidates, selecting a different target object to grasp or controlling, by the at least one computing device, the robotic device to drive to a new position closer to the target object.”
Marchese, in the same field of endeavor, discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contact points using estimated seal quality metrics. Marchese teaches “performing at least one action comprises selecting a different grasp candidate from the set of grasp candidates” (at least as in paragraph 0084, wherein “where none of the candidate contact points produced a seal quality metric that exceeds the predefined threshold level of seal quality, the robotic control component 315 could perform an alternate action to retrieve the item. For example, the robotic control component 315 could select additional candidate contact points and calculate estimated seal quality metrics for these additional contact points using the techniques described herein”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of selecting an alternate candidate contact point in response to the seal quality metric not meeting the threshold, since Marchese teaches wherein the use of estimated seal quality metrics for candidate contact points significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item, thus, reducing possible delay of the workflow and improving environment safety.
Regarding claim 12, the above combination of Chitta, Fan, and Marchese teaches the method of claim 11, however, Chitta does not specifically disclose “wherein selecting a different grasp candidate from the set of grasp candidates comprises selecting the grasp candidate with a next highest grasp quality.”
Marchese, in the same field of endeavor, discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contact points using estimated seal quality metrics. Marchese teaches “wherein selecting a different grasp candidate from the set of grasp candidates comprises selecting the grasp candidate with a next highest grasp quality” (at least as in paragraph 0084, wherein “where none of the candidate contact points produced a seal quality metric that exceeds the predefined threshold level of seal quality, the robotic control component 315 could . . . select additional candidate contact points and calculate estimated seal quality metrics for these additional contact points using the techniques described herein”; therefore, at least as in paragraph 0083, the “robotic control component 315 selects a candidate contact point having the greatest estimated seal quality metric” among the additional candidate contact points).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of selecting an alternate candidate contact point with the next highest seal quality metric, since Marchese teaches wherein the use of estimated seal quality metrics for candidate contact points significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item, thus, reducing possible delay of the workflow and improving environment safety.
Regarding claim 13, the above combination of Chitta and Fan discloses the method of claim 10, however, Chitta does not specifically disclose “wherein determining whether the selected grasp candidate is feasible is based, at least in part, on at least one obstacle located in an environment of the robotic device and/or a movement constraint of an arm of the robotic device that includes the suction-based gripper.”
Marchese, in the same field of endeavor, discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contact points using estimated seal quality metrics. Marchese teaches “wherein determining whether the selected grasp candidate is feasible is based, at least in part, on at least one obstacle located in an environment of the robotic device and/or a movement constraint of an arm of the robotic device that includes the suction-based gripper” (at least as in paragraph 0060, wherein “The robotic control optimization component 315 could then evaluate the different candidate contact points to determine whether any of the candidate contact points are acceptable for use in retrieving the item using a suction end effector and, if so, which of the candidate contact points is optimal for use in picking up the item using the suction end effector”; at least as in paragraph 0081, wherein “the robotic control optimization component 315 can take into account other objects surrounding the item 1005 when determining the exposed surfaces. For example, in the depicted embodiment, the robotic picking arm 210 may not have sufficient room to maneuver a suction end effector onto the side surfaces of the item 1005, due to the storage container 1007 surrounding the item 1005. As a result, in the depicted example, the robotic control optimization component 315 has selected the upwards-facing surface of the item 1005 as the only exposed surface and has selected a number of candidate contact points 1105(1)-(N) on the surface”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of robotic control optimization component determining evaluating the environment, since Marchese teaches wherein robotic control optimization component significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item, thus, reducing possible delay of the workflow and improving environment safety.
Claim(s) 14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chitta et al. (US 20200047331 A1, hereinafter Chitta) in view of Fan (US 20220072707 A1) and Marchese et al. (US 20200398441 A1, hereinafter Marchese), and further in view of Chavez et al. (US 20200316782 A1, hereinafter Chavez).
Regarding claim 14, in view of the above combination of Chitta and Fan, Chitta further discloses the method of claim 1, further comprising:
measuring a grasp quality between the suction-based gripper and the target object after controlling the robotic device to attempt to grasp the target object (at least as in paragraph 0028, wherein “grippers 200 often include off-the-shelf part-presence sensors 220 (e.g. optical sensors) that are built into the gripper 200 to detect the presence or absence of a box 22 after picking the box 22. This allows for independent confirmation of the success or failure of a grasp by the robot 100. For mixed-SKU box picking, the part-presence sensor 220 needs to be properly positioned relative to the box 22 being picked so that the part-presence sensor 220 will react appropriately to the presence or absence of the box 22”) . . .
However, Chitta does not specifically disclose “selecting, by the at least one computing device, a different grasp candidate from the set of grasp candidates when the measured grasp quality is less than a threshold amount; and
controlling the robotic device to lift the target object when the measured grasp quality is greater than the threshold amount.”
Marchese discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contract points using estimated seal quality metrics. Marchese teaches “selecting, by the at least one computing device, a different grasp candidate from the set of grasp candidates when the measured grasp quality is less than a threshold amount” (at least as in paragraph 0024, wherein “In the event that the picking operation fails (e.g., the robotic picking arm fails to lift the item, the robotic picking arm drops the item en route to the item's destination, etc.), the control logic could then select a different candidate contact point and attempt to lift the item again using the suction device end effector. The control logic could also change other parameters, such as the speed at which the robotic picking arm moves once the item has been lifted using the suction device end effector, and can even attempt to lift the item using other end effectors (e.g., a robotic claw that grasps the item)”).
Chavez, in the same field of endeavor, discloses a robotic system configured to determine various grasp strategies and corresponding scores based on the probability of success for each grasp strategy. Chavez teaches “controlling the robotic device to lift the target object when the measured grasp quality is greater than the threshold amount” (at least as in paragraph 0077, wherein “In the event the mass detected by the force sensor increases, and this increase is different from either a static threshold or a generic estimate based on the volume/appearance of the object, the robotic system determines that the wrong object was grasped and drops the object. In some embodiments, the end effector of the robotic system has a pressure sensor or senses deformation of a skin surface of the object using capacitance to determine whether the object has been grasped. The output of the sensor is compared to a grasp threshold to determine whether the feature associated with the object has been grasped . . . the robotic system uses a suction gripper end effector to pick the object and detects a suction-pressure change using a pressure sensor. The robotic system may determine whether or not the object has been grasped based on an output of the pressure sensor”; at least as in paragraph 0080-0081, wherein “In the event the grasp is not successful . . . a different grasping technique is implemented . . . The grasping technique of the plurality of grasping techniques having the highest score is initially selected”; additionally as in paragraph 0064, wherein “the selected grasp is performed/attempted . . . the end effector is a suction gripper and the robotic system detects a suction-pressure change using a pressure sensor to determine whether or not the feature has been grasped”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of selecting a candidate contact point having the greatest estimated seal quality metric and Chavez’ teaching of determining whether grasp threshold is reached and executing another grasp strategy, since Marchese teaches wherein the use of estimated seal quality metrics for candidate contact points significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item thus reducing possible delay of the workflow and improving environment safety and Chavez teaches wherein the robotic grasp system increases the probability of success of grasp operations thus improving throughput, worker safety, and the amount of damage to packages.
Regarding claim 19, in view of the above combination of Chitta and Fan, Chitta further discloses the robotic device of claim 16, wherein the at least one computing device is further configured to:
measure a grasp quality between the suction-based gripper and the target object after controlling the arm of the robotic device to attempt to grasp the target object (at least as in paragraph 0028, wherein “grippers 200 often include off-the-shelf part-presence sensors 220 (e.g. optical sensors) that are built into the gripper 200 to detect the presence or absence of a box 22 after picking the box 22. This allows for independent confirmation of the success or failure of a grasp by the robot 100. For mixed-SKU box picking, the part-presence sensor 220 needs to be properly positioned relative to the box 22 being picked so that the part-presence sensor 220 will react appropriately to the presence or absence of the box 22”) . . .
However, Chitta does not specifically disclose “select a different grasp candidate from the set of grasp candidates when the measured grasp quality is less than a threshold amount; and
control the robotic arm to lift the target object when the measured grasp quality is greater than the threshold amount.”
Marchese discloses a system for controlling a robotic picking arm with a suction device to retrieve an item by holding the item at a selected contact point chosen among a plurality of candidate contract points using estimated seal quality metrics. Marchese teaches “select a different grasp candidate from the set of grasp candidates when the measured grasp quality is less than a threshold amount” (at least as in paragraph 0024, wherein “In the event that the picking operation fails (e.g., the robotic picking arm fails to lift the item, the robotic picking arm drops the item en route to the item's destination, etc.), the control logic could then select a different candidate contact point and attempt to lift the item again using the suction device end effector. The control logic could also change other parameters, such as the speed at which the robotic picking arm moves once the item has been lifted using the suction device end effector, and can even attempt to lift the item using other end effectors (e.g., a robotic claw that grasps the item)”).
Chavez, in the same field of endeavor, discloses a robotic system configured to determine various grasp strategies and corresponding scores based on the probability of success for each grasp strategy. Chavez teaches “control the robotic arm to lift the target object when the measured grasp quality is greater than the threshold amount” (at least as in paragraph 0077, wherein “In the event the mass detected by the force sensor increases, and this increase is different from either a static threshold or a generic estimate based on the volume/appearance of the object, the robotic system determines that the wrong object was grasped and drops the object. In some embodiments, the end effector of the robotic system has a pressure sensor or senses deformation of a skin surface of the object using capacitance to determine whether the object has been grasped. The output of the sensor is compared to a grasp threshold to determine whether the feature associated with the object has been grasped . . . the robotic system uses a suction gripper end effector to pick the object and detects a suction-pressure change using a pressure sensor. The robotic system may determine whether or not the object has been grasped based on an output of the pressure sensor”; at least as in paragraph 0080-0081, wherein “In the event the grasp is not successful . . . a different grasping technique is implemented . . . The grasping technique of the plurality of grasping techniques having the highest score is initially selected”; additionally as in paragraph 0064, wherein “the selected grasp is performed/attempted . . . the end effector is a suction gripper and the robotic system detects a suction-pressure change using a pressure sensor to determine whether or not the feature has been grasped”).
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Chitta, to include Marchese's teaching of selecting a candidate contact point having the greatest estimated seal quality metric and Chavez’ teaching of determining whether grasp threshold is reached and executing another grasp strategy, since Marchese teaches wherein the use of estimated seal quality metrics for candidate contact points significantly reduces the robotic arm’s rate of failing to retrieve the item or likelihood of dropping the item thus reducing possible delay of the workflow and improving environment safety and Chavez teaches wherein the robotic grasp system increases the probability of success of grasp operations thus improving throughput, worker safety, and the amount of damage to packages.
Conclusion
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/RICARDO I VISCARRA/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657