# US 11,440,194 B2 · Physical human-robot interaction (pHRI) > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,440,194 B2 - Document ID: US11440194B2 - Status: Granted - Kind: B2 - Application number: 16/817,033 - Filed: 2020-03-12 - Published: 2021-03-18 - Granted: 2022-09-13 - Earliest claimed: 2019-09-13 - Assignees: Honda Motor Co., Ltd. - Applicants: Honda Motor Co., Ltd. - Inventors: Katsu Yamane; Joseph A. Campbell - Web page: https://patentgrep.com/patent/US11440194B2 ## CPC - B25J 9/1694: {characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion} - B25J 9/12: electric - B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor} - B25J 9/163: {learning, adaptive, model based, rule based expert control} - B25J 11/0005: {Manipulators having means for high-level communication with users, e.g. speech generator, face recognition means} - B25J 13/085: {Force or torque sensors (B25J13/082, B25J13/084 take precedence)} - B25J 19/02: Sensing devices - B25J 19/023: {including video camera means} - B25J 9/0006: {Exoskeletons, i.e. resembling a human figure} - G05B 2219/40202: Human robot coexistence - G05B 2219/40413: Robot has multisensors surrounding operator, to understand intention of operator - G05B 2219/40414: Man robot interface, exchange of information between operator and robot - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract A robot for physical human-robot interaction may include a number of sensors, a processor, a controller, an actuator, and a joint. The sensors may receive a corresponding number of sensor measurements. The processor may reduce a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generate an updated sensor measurement dataset. The processor may receive an action associated with a human involved in pHRI with the robot. The processor may generate a response for the robot based on the updated sensor measurement dataset and the action. The controller may implement the response via an actuator within a joint of the robot. ## Claims ### Claim 1 A robot for physical human-robot interaction (pHRI), comprising: a number of sensors receiving a corresponding number of sensor measurements; a processor reducing a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generating an updated sensor measurement dataset, wherein the processor receives an action associated with a human involved in pHRI with the robot, wherein the processor generates a response for the robot based on the updated sensor measurement dataset and the action; and a controller implementing the response via an actuator within a joint of the robot, wherein temporal sparsity is indicative of useful data from the sensor measurements over a period of time associated with the pHRI utilized to generate the response for the robot, and wherein spatial sparsity is indicative of useful data from a subset of the number of sensor measurements utilized to generate the response for the robot. ### Claim 2 (depends on 1) The robot for pHRI of claim 1, wherein the processor generates the response for the robot based on a Bayesian Interaction Primitives (BIP) model. ### Claim 3 (depends on 2) The robot for pHRI of claim 2, wherein the BIP model is approximated using a Monte Carlo Ensemble. ### Claim 4 (depends on 1) The robot for pHRI of claim 1, wherein the processor reduces the dimensionality of the number of sensor measurements based on a weighted linear combination of one or more time-dependent basis functions. ### Claim 5 (depends on 4) The robot for pHRI of claim 4, wherein a basis space for one or more time-dependent basis functions is calculated via Orthogonal Least Squares (OLS). ### Claim 6 (depends on 1) The robot for pHRI of claim 1, wherein the processor reduces the dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors by masking data associated with sensors during time periods when the sensors are not triggered. ### Claim 7 (depends on 1) The robot for pHRI of claim 1, wherein the processor reduces the dimensionality of the number of sensor measurements based on spatial sparsity associated with the number of sensors determined based on mutual information. ### Claim 8 (depends on 1) The robot for pHRI of claim 1, wherein the sensors of the number of sensors are force sensors providing force measurements. ### Claim 9 (depends on 1) The robot for pHRI of claim 1, wherein the sensors of the number of sensors are motion sensors providing motion measurements. ### Claim 10 (depends on 1) The robot for pHRI of claim 1, wherein the updated sensor measurement dataset is generated by performing feature selection from the sensor measurements. ### Claim 11 A method for physical human-robot interaction (pHRI), comprising: receiving a number of sensor measurements from a corresponding number of sensors; reducing a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generating an updated sensor measurement dataset; receiving an action associated with a human involved in pHRI with a robot; generating a response for the robot based on the updated sensor measurement dataset and the action; and implementing the response via an actuator within a joint of the robot, wherein temporal sparsity is indicative of useful data from the sensor measurements over a period of time associated with the pHRI utilized to generate the response for the robot, and wherein spatial sparsity is indicative of useful data from a subset of the number of sensor measurements utilized to generate the response for the robot. ### Claim 12 (depends on 11) The method for pHRI of claim 11, comprising generating the response for the robot based on a Bayesian Interaction Primitives (BIP) model. ### Claim 13 (depends on 12) The method for pHRI of claim 12, wherein the BIP model is approximated using a Monte Carlo Ensemble. ### Claim 14 (depends on 11) The method for pHRI of claim 11, comprising reducing the dimensionality of the number of sensor measurements based on a weighted linear combination of one or more time-dependent basis functions. ### Claim 15 (depends on 14) The method for pHRI of claim 14, comprising calculating a basis space for one or more time-dependent basis functions based on Orthogonal Least Squares (OLS). ### Claim 16 (depends on 11) The method for pHRI of claim 11, comprising reducing the dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors by masking data associated with sensors during time periods when the sensors are not triggered. ### Claim 17 (depends on 11) The method for pHRI of claim 11, comprising reducing the dimensionality of the number of sensor measurements based on spatial sparsity associated with the number of sensors determined based on mutual information. ### Claim 18 (depends on 11) The method for pHRI of claim 11, wherein the sensors of the number of sensors are force sensors providing force measurements. ### Claim 19 (depends on 11) The method for pHRI of claim 11, wherein the sensors of the number of sensors are motion sensors providing motion measurements. ### Claim 20 A system for physical human-robot interaction (pHRI), comprising: a number of sensors receiving a corresponding number of sensor measurements; a processor reducing a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generating an updated sensor measurement dataset, wherein the processor receives an action associated with a human involved in pHRI with a robot, wherein the processor generates a response for the robot based on the updated sensor measurement dataset and the action associated with the human; and a controller implementing the response via an actuator within a joint of the robot, wherein temporal sparsity is indicative of useful data from the sensor measurements over a period of time associated with the pHRI utilized to generate the response for the robot, and wherein spatial sparsity is indicative of useful data from a subset of the number of sensor measurements utilized to generate the response for the robot.