Abstract: A method includes: obtaining current motion state data of the wheel-legged robot, the current motion state data representing motion features of the wheel-legged robot, inputting the current motion state data into a nonlinear controller to obtain a target joint angular acceleration reference value of a target robot joint of the wheel-legged robot, and inputting the target joint angular acceleration reference value into a whole-body dynamics controller to output a joint torque for controlling the wheel-legged robot to perform a control task.

Abstract: In an embodiment a robot control apparatus includes at least one first sensor provided on a front of a head part of a robot including a wheel part and the head part connected to an upper portion of the wheel part, the first sensor being configured to sense a first distance from a ground, at least one second sensor provided on a rear of the head part and configured to sense a second distance from the ground and a controller configured to determine a driving environment of the robot based on at least one of the first distance or the second distance and adjust a control gain related to a balance control of the robot based on the determined driving environment.

Abstract: A transport system for containers in the beverage industry, with a first container conveyor including a conveyor belt for conveying the containers, and a lubrication system including an applicator for applying a lubricant to the conveyor belt of the first container conveyor, wherein the transport system comprises a second container conveyor with a further conveyor belt for conveying the containers, and the lubrication system includes a mobile robot which has the applicator arranged thereon and a floor-supported undercarriage, for applying the lubricant in an automated selectively to the conveyor belt of the first container transporter or the conveyor belt of the second container conveyor.

Abstract: A method for executing a process, in particular using at least one robot, includes executing a run-through of the process, detecting a value of a first process variable, and detecting an assessment of this executed process run-through. Assessment learning steps are then repeated multiple times, wherein run-throughs of the process using varied process controls are executed and additional assessments are detected. A first quality factor model of the process, which model determines a quality factor for the process on the basis on the first process variable, is machine-learned based on the detected assessments and values of the first process variable. The method further includes repeating process control optimization steps multiple times.

Abstract: Methods and apparatus for an end-of-arm tool having multiple robotic grippers, such as two or more grippers, and coordination of motion of the multiple grippers, so as to allow for automated testing and packaging of scalable bags conveyed to an inspection station.

Abstract: A seed camera disposed a first location is manually calibrated. A second camera, disposed at a second location, detects a physical marker based on predefined characteristics of the physical marker. The physical marker is located within an overlapping field of view between the seed camera and the second camera. The second camera is calibrated based on a combination of the physical location of the physical marker, the first location of the seed camera, the second location of the second camera, a first image of the physical marker generated with the seed camera, and a second image of the physical marker generated with the second camera.

Abstract: The robot includes a storage unit and a control unit. The control unit acquires outside stimulus feature amounts that are feature amounts of an outside stimulus acting from outside, stores the acquired outside stimulus feature amounts in the storage unit as a history, compares outside stimulus feature amounts acquired at a certain timing with outside stimulus feature amounts stored in the storage unit to calculate a first similarity degree, and controls operations based on the calculated first similarity degree.

Abstract: According to one aspect, an adaptive driving style system may include a set of two or more sensors, a memory, and a processor. The set of two or more sensors may receive two or more sensor signals. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, including training a trust model using two or more of the sensor signals as input, training a preference model using the trust model and two or more of the sensor signals as input, and generating a driving style preference based on an adaptive driving style model including the trust model and the preference model.

Abstract: A scheduling system includes first circuitry and second circuitry. The first circuitry stores, in a memory, information on a plurality of tasks to be executed by at least one mobile device. The information on the plurality of tasks includes information on an estimated amount of battery consumption of the at least one mobile device in executing each of the plurality of tasks. The second circuitry receives designation of the plurality of tasks to be executed by the at least one mobile device. The second circuitry further causes a display to display a screen having a schedule in which the plurality of tasks is arranged for the at least one mobile device based on the information on the estimated amount of battery consumption.

Abstract: The present invention relates to methods for learning a robot task and robots systems using the same. A robot system may include a robot configured to perform a task, and detect force information related to the task, a haptic controller configured to be manipulatable for teaching the robot, the haptic controller configured to output a haptic feedback based on the force information while teaching of the task to the robot is performed, a sensor configured to sense first information related to a task environment of the robot and second information related to a driving state of the robot, while the teaching is performed by the haptic controller for outputting the haptic feedback, and a computer configured to learn a motion of the robot related to the task, by using the first information and the second information, such that the robot autonomously performs the task.

Abstract: Techniques for Meta-Q-Learning (MQL) are described. A method of MQL may include receiving a request from an agent to perform adaptation based at least on task data associated with a new task collected by the agent, identifying a subset of meta-training data corresponding to the task data in a replay buffer, and adapting a policy using the subset of meta-training data and the task data to generate an adapted policy, wherein the adapted policy is used identify a next action for the agent to perform.

Abstract: A camera tracking system receives patient reference tracking information indicating pose of a patient reference array tracked by a patient tracking camera relative to a patient reference frame. A local XR headset view pose transform is determined between a local XR headset reference frame and the patient reference frame. Remote reference tracking information is received indicating pose of a remote reference array tracked by a remote reference tracking camera. A remote XR headset view pose transform is determined between a remote XR headset reference frame of a remote XR headset and the remote reference array. A 3D computer image is transformed from a local pose determined using the local XR headset view pose transform to a remote pose determined using the remote XR headset view pose transform. The transformed 3D computer image is provided to the remote XR headset for display with the remote pose relative to the remote XR headset reference frame.

Abstract: A system for performing interactions within a physical environment including: a robot base that undergoes movement relative to the environment; a robot arm mounted to the robot base, the robot arm including an end effector mounted thereon; a first tracking system that measures a robot base position; a second tracking system that measures movement of the robot base; and, a control system that uses a robot base position to at least partially control the robot arm to move the end effector along an end effector path, wherein the control system: determines the robot base position at least in part using signals from the first tracking system; and, in the event of failure of the first tracking system: determines a robot base position using signals from the second tracking system; and, controls the robot arm to move the end effector along the end effector path at a reduced end effector speed.

Abstract: Systems and methods for identifying surgery tools of a robotic surgery system in surgery video based on robot kinematics are described. Kinematic data describing a position of a first robotic surgery arm including a surgery tool is used to determine a nominal position of the first robotic surgery arm. A search area within image data obtained during a robotic surgery from an endoscope attached to a second robotic surgery arm is determined based on the kinematic data and a nominal position of the surgery tool. The surgery tool is identified within the search area of the image data using an object detection technique.

Abstract: A method includes receiving sensor data collected by an autonomous mobile robot as the autonomous mobile robot moves about an environment, the sensor data being indicative of sensor events and locations associated with the sensor events. The method includes identifying a subset of the sensor events based on the locations. The method includes providing, to a user computing device, data indicative of a recommended behavior control zone in the environment, the recommended behavior control zone containing a subset of the locations associated with the subset of the sensor events. The method includes defining, in response to a user selection from the user computing device, a behavior control zone such that the autonomous mobile robot initiates a behavior in response to encountering the behavior control zone, the behavior control zone being based on the recommended behavior control zone.

Abstract: A camera tracking system receives patient reference tracking information indicating pose of a patient reference array tracked by a patient tracking camera relative to a patient reference frame. A local XR headset view pose transform is determined between a local XR headset reference frame and the patient reference frame. Remote reference tracking information is received indicating pose of a remote reference array tracked by a remote reference tracking camera. A remote XR headset view pose transform is determined between a remote XR headset reference frame of a remote XR headset and the remote reference array. A 3D computer image is transformed from a local pose determined using the local XR headset view pose transform to a remote pose determined using the remote XR headset view pose transform. The transformed 3D computer image is provided to the remote XR headset for display with the remote pose relative to the remote XR headset reference frame.

Abstract: A technique for training a neural network, including generating a plurality of input vectors based on a first plurality of task demonstrations associated with a first robot performing a first task in a simulated environment, wherein each input vector included in the plurality of input vectors specifies a sequence of poses of an end-effector of the first robot, and training the neural network to generate a plurality of output vectors based on the plurality of input vectors. Another technique for generating a task demonstration, including generating a simulated environment that includes a robot and at least one object, causing the robot to at least partially perform a task associated with the at least one object within the simulated environment based on a first output vector generated by a trained neural network, and recording demonstration data of the robot at least partially performing the task within the simulated environment.

Abstract: A hand device is attached to a robot arm, grips a workpiece extending helically around a helical axis, and includes a base attached to the robot arm and a gripping part that is supported by the base in a rotatable manner around a predetermined rotation axis and that grips the workpiece. The gripping part grips the workpiece on the predetermined rotation axis such that the helical axis of the workpiece substantially extends along the predetermined rotation axis, and rotates in a helical direction of the workpiece in accordance with an external force acting on the workpiece in a tangential direction around the predetermined rotation axis.

Abstract: Presented herein are embodiments of a two-stage methodology that integrates data-driven imitation learning and model-based trajectory optimization to generate optimal trajectories for autonomous excavators. In one or more embodiments, a deep neural network using demonstration data to mimic the operation patterns of human experts under various terrain states, including their geometry shape and material type. A stochastic trajectory optimization methodology is used to improve the trajectory generated by the neural network to ensure kinematics feasibility, improve smoothness, satisfy hard constraints, and achieve desired excavation volumes. Embodiments were tested on a Franka robot arm equipped with a bucket end-effector. Embodiments were also evaluated on different material types, such as sand and rigid blocks. Experimental results showed that embodiments of the two-stage methodology that comprises combining expert knowledge and model optimization increased the excavation weights by up to 24.

Abstract: Systems and methods for monitoring a surgical procedure are provided. A coordinate system of a first robotic arm and a second robotic arm may be co-registered or correlated to each other. One or more poses of an imaging device may be determined to provide real-time intraoperative imaging of a region of interest during a surgical procedure. Anatomical elements may be identified in the real-time images of the region of interest from which a surgical tool should maintain a predetermined distance. The surgical tool may be prevented from approaching the identified anatomical elements by less than a predetermined distance using the co-registration of the coordinate systems.

Abstract: An oversized representation of at least a portion of a robot is filtered (e.g., voxels are set as unoccupied for any objects that reside completely within the oversized representation) from a representation of an operational environment, which provides a digital model of the operational environment which can, for example, be used for motion planning for the robot. The oversized representation exceeds a physical dimension of at least a portion (e.g. appendage) of the robot, to advantageously account for cables and other features that are attached to, and extending beyond, the outer dimensions of the robot. The specific dimensions of the oversized representation can be based on a variety of factors, for example a geometry of the cable, orientation or position of the robot appendage, orientation or position of the cable with respect to the robot appendage, velocity of the appendage, slack in the cable, etc., which may be modeled.

Abstract: A welding robot is provided. The welding robot is adapted for operating in the space between an upper and a lower platen of a press and comprises a support frame. A welding tool is movable mounted to the support frame. A grinding tool is movable mounted to the support frame. At least a camera is adapted for capturing a view of a working area. A processor is adapted for executing executable commands stored in a storage medium connected thereto. The processor when executing the commands identifies defects on a surface of one of the upper and the lower platen based on image data received from the at least a camera and controls the welding tool and the grinding tool in dependence on the image data. The processor receives data indicative of the repair area, automatically determines toolpath data for welding and grinding in dependence upon the data indicative of a repair area, and generates and provides control data for controlling the welding tool and the grinding tool in dependence upon the toolpath data.

Abstract: A robot system includes first and third robots arranged along a transport direction in which a workpiece is conveyed, and a second robot arranged between the first and third robots along the transport direction. Each of the first and third robots has a first distal end at which a first end effector is configured to be provided to work on the workpiece and has a plurality of axes arranged in a first axis configuration in which a rotation axis, a pivot axis, a pivot axis, and a rotation axis are arranged in this order from the first distal end. The second robot has a second distal end at which a second end effector is configured to be provided to work on the workpiece. The second robot has a second axis configuration different from the first axis configuration.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically cleaning a portion of an environment. One of the methods includes detecting, by an automated cleaning system, movement within a physical environment; determining, by the automated cleaning system and using data that represents the movement, that one or more threshold criteria for cleaning at least a portion of the physical environment are satisfied; and in response to determining that the one or more threshold criteria for cleaning at least the portion of the physical environment are satisfied, sending, by the automated cleaning system, an instruction to a device in the physical environment to cause the device to initiate a cleaning process of at least the portion of the physical environment.

Abstract: Systems and methods of manipulating/controlling robots. In many scenarios, data collected by a sensor (connected to a robot) may not have very high precision (e.g., a regular commercial/inexpensive sensor) or may be subjected to dynamic environmental changes. Thus, the data collected by the sensor may not indicate the parameter captured by the sensor with high accuracy. The present robotic control system is directed at such scenarios. In some embodiments, the disclosed embodiments can be used for computing a sliding velocity limit boundary for a spatial controller. In some embodiments, the disclosed embodiments can be used for teleoperation of a vehicle located in the field of view of a camera.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method for operating an autonomous vehicle comprises receiving real-time perception data at a planner module located on the autonomous vehicle, and generating an initial motion plan based on the real-time perception data. The autonomous vehicle is maneuvered along a constrained route based on the initial motion plan without user input. An alert condition is detected from the real-time perception data, and a notification is displayed at an operator device. The notification includes a request for user input to adjust the initial motion plan. User input is received at the planner module and a modified motion plan is generated by adjusting the initial motion plan based on the user input. A virtual representation of objects identified by the AV is locally rendered for display at the operator device.

Abstract: A method for executing heterogeneous program units of a program, in particular for controlling an automated system, includes forming, using a central management unit, an execution unit including program sequences having a plurality of instances of the heterogeneous program units. The heterogeneous program units are chosen from a set of the heterogeneous program units which are registered on the central management unit. The method further includes executing the plurality of instances of the heterogeneous program units of the execution unit according to a configured sequence.

Abstract: Robot charging dock includes a charge connector configured to mate with a charging port of a mobile robot. There is a charge connector frame having a front surface on which the charge connector is mounted. The front surface has a first side edge and a second side edge. There is a front cover disposed over the charge connector frame which has an aperture through which the charge connector protrudes. At least a portion of the front cover is spaced from the front surface of the charge connector frame, defining an internal region. There is an opening to the internal region formed along at least a portion of a perimeter of the aperture and there is a light source disposed in the internal region. The light source is directed toward the opening to allow the light source to illuminate charge connector.

Abstract: Provided is a machine-learning device which can efficiently perform machine learning. The machine-learning device comprises: a vision execution unit which captures an image of an object W by means of a visual sensor by executing a vision execution command from a robot program, and detects or determines the object W from the captured image; a result acquisition unit which acquires the detection result or the determination result for the object W by executing a result acquisition command from the robot program; an additional annotation unit which gives a label to the captured image on the basis of the detection result or the determination result for the image of the object W by executing an annotation command from the robot program, and acquires new training data; and a learning unit which performs machine learning by using the new training data by executing a learning command from the robot program.

Abstract: Disclosed techniques for decreasing teach times of robot systems may obtain a first set of parameters of a first trained robot-control model of a first robot trained to perform a task and determine, based on the first set of parameters, a second set of parameters of a second robot-control model of a second robot before the second robot is trained to perform the task. In some cases, a plurality of sets of parameters from trained robot-control models of respective robots trained to perform a task may be obtained. Thus, for example, a convergence of values of those parameters on a value, or range of potential values, may be determined. Embodiments may determine values for parameters of the control model of the (e.g., second) robot to be trained within a range, or a threshold, based on values of corresponding parameters of the trained robot(s).

Abstract: A deformable sensor comprises an enclosure comprising a deformable membrane, the enclosure configured to be filled with a medium, and an imaging sensor, disposed within the enclosure, having a field of view configured to be directed toward a bottom surface of the deformable membrane. The imaging sensor is configured to capture an image of the deformable membrane. The deformable sensor is configured to determine depth values for a plurality of points on the deformable membrane based on the image captured by the imaging sensor and a trained neural network.

Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

Abstract: A system includes: a first module configured to, based on a set of target robot joint angles, generate a first estimated end effector pose and a first estimated latent variable that is a first intermediate variable between the set of target robot joint angles and the first estimated end effector pose; a second module configured to determine a set of estimated robot joint angles based on the first estimated latent variable and a target end effector pose; a third module configured to determine joint probabilities for the robot based on the first estimated latent variable and the target end effector pose; and a fourth module configured to, based on the set of estimated robot joint angles, determine a second estimated end effector pose and a second estimated latent variable that is a second intermediate variable between the set of estimated robot joint angles and the second estimated end effector pose.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network. One of the methods includes receiving an observation characterizing a current state of the environment; determining a target network output for the observation by performing a look ahead search of possible future states of the environment starting from the current state until the environment reaches a possible future state that satisfies one or more termination criteria, wherein the look ahead search is guided by the neural network in accordance with current values of the network parameters; selecting an action to be performed by the agent in response to the observation using the target network output generated by performing the look ahead search; and storing, in an exploration history data store, the target network output in association with the observation for use in updating the current values of the network parameters.

Abstract: A robot is disclosed. The robot comprises: a body including a transport means; a head disposed on the body and including a plurality of sensors; and a processor which controls the head on the basis of at least one sensor among the plurality of sensors and acquires information related to a user's health through the sensor of the controlled head.

Abstract: An autonomous mobile device (AMD) that interacts with a user operates more effectively when able to persist an entity identifier across time with a particular user. Sensor data, such as a set of image frames, is acquired over time and used to determine locations of the user in the physical space. An entity identifier may be associated with the user as depicted in each frame, to allow the AMD to distinguish one user from another. However, due to changes in orientation, appearance, lighting, and so forth, different entity identifiers may be assigned across different frames, even though the user is the same. Information about the location at different times is used to establish a relationship between a root entity identifier and the entity identifiers in individual frames, allowing for consistent operation with respect to the particular user associated with the root entity identifier.

Abstract: In a method of operation of a robot, the robot identifies a set of candidate actions that may be performed by the robot, and collects, for each candidate action of the set of candidate actions, a respective set of ancillary data. The robot transmits a request for instructions to a tele-operation system that is communicatively coupled to the robot. The request for instructions includes each candidate action and each respective set of ancillary data. The robot receives, and executes, the instructions from the tele-operation system. The robot updates a control model, based at least in part on each candidate action, each respective set of ancillary data, and the instructions, to increase a level of autonomy of the robot. The robot may transmit the request for instructions to the tele-operation system in response to determining the robot is unable to select a candidate action to perform in furtherance of an objective.

Abstract: A method for improving a cycle time of a process of a product is provided. The method includes: collecting process profile data from a plurality of tool groups running the process, and calculating values of a plurality of key-performance-indicators (KPIs) of each tool group including calculating a standard deviation of an output of a stage of a bottleneck tool group of the tool groups; feeding the values of the KPIs and a work-in-progress (WIP) of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model; selecting a set of major KPIs of each tool group from the KPIs according to the impact of each tool group; and controlling the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP.

Abstract: Some aspects include a method for operating a robot in a workspace, including: capturing, with an image sensor, image data of the workspace including objects within the workspace as the robot moves within the workspace; identifying, with a processor of the robot, at least one characteristic in the image data, wherein the at least one characteristic comprises one of: an edge, a shape, and a color; determining, with the processor, an object type of an object; and instructing, with the processor, the robot to execute at least one action based on the at least one characteristic, wherein the at least one action comprises one of: driving along a modified path and driving around the object.

Abstract: A system connects an assembly with at least one actuator and at least one sensor to a controller. The assembly is guided by a handling apparatus. The controller includes a radio master spatially separate from the handling apparatus and has a sending and receiving unit. The assembly is supplied with supply voltage by the handling apparatus. In or on the assembly, the supply voltage is transformed into a direct current target voltage with the aid of a DC/DC or AC/DC transformer. The direct current target voltage supplies the sending and receiving unit of the assembly.

Abstract: A deep learning framework application database server includes: an input/output unit configured to receive an inference query from a user; a storage unit serving as a database and comprising a previously learned first learning model table and an inference dataset table associated with the inference query; and a framework unit configured to interwork with the database and perform deep learning for the inference query using the first learning model table and the inference dataset table.

Abstract: A system for providing a behavior simulation platform service is provided, and comprises: a user terminal that selects and downloads at least one behavior simulation program, and then applies the at least one behavior simulation program to a previously owned robot; and a platform service provision server including a database unit that maps and stores the at least one behavior simulation program and at least one robot, a transmission unit that, if the user terminal selects a behavior simulation program, transmits the selected behavior simulation program to the user terminal, and a storage unit that, if feedback is received from the previously owned robot via the user terminal, stores the feedback.

Abstract: An operation rule determination device includes an environment execution unit that obtains a state of a control target after each operation and the degree associated with the state for a series of operations on the control target, by using degree information in which the state and the degree of desirability of the state are associated with each other, and a risk-considered history generation unit that calculates a cumulative degree obtained by accumulating the obtained degree for the series of operations, and, when the cumulative degree satisfies a condition, reduces the degree associated with the state after the series of operations in the degree information.

Abstract: The invention relates to an assembly for the positioning and position detection of a deformable load-bearing plate, having a driving parallel kinematics system with a plurality of drive legs, wherein the load-bearing plate forms a constituent part of the driving parallel kinematics system or is fixed rigidly on a platform thereof, a reference parallel kinematics system with a substantially deformation-free reference plate which is formed by the platform of the reference parallel kinematics system or is fixed rigidly thereto and which is positioned underneath the load-bearing plate, and a plurality of reference legs, a sensor assembly which comprises a plurality of leg length measurement devices arranged in the reference legs and also a plurality of distance sensors arranged on the reference plate for point-wise or region-wise detection of the distance of the reference plate from the load-bearing plate, and a position-calculating unit which is connected at the input side to the leg length measurement devices a

Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.

Abstract: For teleoperation of a surgical robotic system, the control of the surgical robotic system accounts for a limited degree of freedom of a tool in a surgical robotic system. A projection from the greater DOF of the user input commands to the lesser DOF of the tool is included within or as part of the inverse kinematics. The projection identifies feasible motion in the end-effector domain. This projection allows for a general solution that works for tools having different degrees of freedom and will converge on a solution.

Abstract: At least one robot is disclosed for object manipulation, on the basis of a probabilistic approach without object detection by visual sensors, a probability distribution is determined approximately by an iterative, recursive application of a Bayes filter algorithm, wherein state transition probabilities obtained, in a nondeterministic motion model for system simulation, are multiplicatively linked with measurement probabilities obtained at the start of the application.

Abstract: A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.

Abstract: A robot balance control method as well as a robot using the same and a computer readable storage medium are provided. In the method, a brand new flywheel model different from the existing flywheel model is created. In this flywheel model, the foot of the support leg of the robot is equivalent to the massless link of the flywheel model, while rest parts of the robot are equivalent to the flywheel of the flywheel model. Compared with the various models in the prior art, this flywheel model is more in line with the actual situation of the robot during the monoped supporting period. By controlling the posture of the foot of the support leg based on this flywheel model, a better balance effect can be achieved, which avoids the overturning of the robot.

Abstract: The present disclosure provides a method comprising receiving by an autonomous vehicle (“AV”) service provider a request for an AV service from a user, wherein the request identifies an origin and a destination for the AV service; identifying a plurality of possible routes between the origin and the destination; and, for each one of the plurality of possible routes assigning to the one of the possible routes at least one of a comfort score, a confidence score, and a risk an risk score based on data associated with the one of the possible routes determining a price for the one of the possible routes based on a combination of the at least one of the comfort score, the confidence score, and the risk score assigned to the one of the possible routes.