Abstract: A motion control method is provided. The motion control method comprises: obtaining a sequence of motion parameters of a joint; interpolating the sequence of motion parameters to obtain an interpolation parameter sequence; and calculating driving parameters of a motion component based on the interpolation parameter sequence to drive the motion component to move. According to the motion control method, only a few of motion parameters need to be set by a user, and a lot of motion parameters can be obtained by interpolation, which make it possible for a motor or a robot to achieve smooth operation while a workload for the user is kept to be lower. A motion control device, system, and storage medium are also provided.

Abstract: Robot scheduling, robot path control and robot fire control methods and devices, a server and a storage medium are provided. The robot scheduling method includes: receiving a scheduling instruction; determining a working state and a current position of a robot in a working area in response to the scheduling instruction; wherein the working state comprises an idle state and a non-idle state; and when the working state of the robot is idle and the current position is outside a preset target range, scheduling the robot from the current position into the target range.

Abstract: An automatic parking system configured to cause an autonomous vehicle to travel autonomously from a parking space to a pickup area based on a parking space departure time determined such that the autonomous vehicle arrives the pickup area at a scheduled pickup time that is predetermined. The automatic parking system includes an on-control time setting unit configured to set an on-control time at which a state of the autonomous vehicle is controlled to be a power-on state based on the parking space departure time, and a power-on control unit configured to control the state of the autonomous vehicle to be the power-on state based on the on-control time that is set. The on-control time setting unit is configured to set the on-control time such that the power-on state is established prior to the parking space departure time.

Abstract: A lab system identifies a set of steps associated with a protocol for a lab meant to be performed by a robot within the lab using equipment and reagents. The lab system renders, within a user interface, a virtual representation of the lab, a virtual robot, and virtual equipment and reagents. Responsive to operating in a first mode, the lab system simulates the identified set of steps identify virtual positions of the virtual robot within the lab as the virtual robot performs the steps and modifies the virtual representation of the lab to mirror the identified positions of the virtual robot in real-time. Responsive to operating in a second mode, the lab system identifies positions of the robot within the lab as the robot performs the identified set of steps and modifies the virtual representation of the lab to mirror the identified positions of the robot in real-time.

Abstract: A robot system (1) includes the robot (10), a motion sensor (11), a surrounding environment sensor (12, 13), an operation apparatus (21), a learning control section (41), and a relay apparatus (30). The robot (10) performs work based on an operation command. The operation apparatus (21) detects and outputs an operator-operating force applied by the operator. The learning control section (41) outputs a calculation operating force. The relay apparatus (30) outputs the operation command based on the operator-operating force and the calculation operating force. The learning control section (41) estimates and outputs the calculation operating force by using a model constructed by performing the machine learning of the operator-operating force, the surrounding environment data, the operation data, and the operation command based on the operation data and the surrounding environment data outputted by the sensors (11 to 13), and the operation command outputted by the relay apparatus (30).

Abstract: A method includes generating a parameter of a trajectory associated with a scenario using a path planner. The parameter is generated based on a training dataset. The method includes comparing the parameter of the trajectory against a validation parameter associated with a validation dataset. The validation parameter is based on human-based vehicle driving trajectory data associated with scenarios that satisfy a level of similarity with the scenario. The method further includes determining a level of similarity between the parameter associated with the scenario and the validation parameter associated with the scenarios, and, subsequent to determining that the level of similarity fails to satisfy a similarity threshold, the method concludes with providing training data associated with the scenario to the training dataset so that a subsequent parameter of a subsequent trajectory generated by the path planner and associated with the scenario satisfies the level of similarity against the validation parameter.

Abstract: A method and computing system for performing object detection are presented. The computing system may be configured to: receive first image information that represents at least a first portion of an object structure of an object in a camera's field of view, wherein the first image information is associate with a first camera pose; generate or update, based on the first image information, sensed structure information representing the object structure; identify an object corner associated with the object structure; cause the robot arm to move the camera to a second camera pose in which the camera is pointed at the object corner; receive second image information associated with the second camera pose; update the sensed structure information based on the second image information; determine, based on the updated sensed structure information, an object type associated with the object; determine one or more robot interaction locations based on the object type.

Abstract: A method for controlling at least one effector trajectory of an effector of a robot for solving a predefined task is proposed. A sequence of postures are acquired to modify at least one of a contact constraint topology and an object constraint topology. A set of constraint equations are generated based on at least one of the modified contact constraint topology and the modified object constraint topology. On the generated set of constraint equations, a constraint relaxation is performed to generate a task description including a set of relaxed constraint equations. The at least one effector trajectory is generated by applying a trajectory generation algorithm on the task description. An inverse kinematics algorithm is performed to generate a control signal from the at least one effector trajectory. At least one effector is controlled to execute the at least one effector trajectory based on the generated control signal.

Abstract: A robot system includes a robot, state detection sensors to, a timekeeping unit, a learning control unit, a determination unit, an operation device, and an input unit, and an additional learning unit. The determination unit determines whether or not the work of the robot can be continued under the control of the learning control unit based on the state values detected by the state detection sensors to and outputs determination result. The additional learning unit performs additional learning of the determination result indicating that the work of the robot cannot be continued, the operator operation force, work state output by the operation device and the input unit, and timer signal output by the timekeeping unit.

Abstract: A diagnostic system includes an acquirer configured to acquire current waveform data representing a waveform relating to a current supplied to a driving device of an apparatus and a determiner configured to determine a degree of abnormality in the apparatus from a varying portion of the waveform, the varying portion corresponding to a varying time period during which a rotation speed of the driving device increases or decreases.

Abstract: A method for a multi-legged robot having a body and a number of legs, includes: obtaining a current pose of the body, forces applied to the body, and joint angles of each of supporting legs of the legs; creating a mapping matrix from the forces applied to the body to desired support forces applied to soles of the supporting legs; obtaining priority targets by prioritizing the forces acting in different directions, determining a weight matrix for each priority target, and creating an optimization model of the support forces for each priority target based on the mapping matrix and the weight matrices; solving the optimization model of each of the priority targets to obtain the desired support forces corresponding to each of the priority targets; and calculating joint torques of the supporting legs for joint control, based on the solved desired support forces and Jacobian matrices corresponding to the supporting legs.