A medical holding apparatus includes: a support including a plurality of arms, and a plurality of joints configured to connect the plurality of arms, the support being configured to support an imaging unit at a distal end thereof; a load applying mechanism arranged in at least one of the joints and configured to apply a resistance load against operation of the at least one of the joints to the support; and a processor comprising hardware, the processor being configured to: set torque to be applied by the load applying mechanism based on an operating state of the imaging unit; and apply a load corresponding to the set torque to the load applying mechanism when a rotation inhibit state of each of the arms of the support is released.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

In accordance with one embodiment, an automated probe system includes a probe configured to be reversibly inserted into a live body part, a robotic arm attached to the probe and configured to manipulate the probe, a first sensor configured to track movement of the probe during an insertion and a reinsertion of the probe in the live body part, a second sensor configured to track movement of the live body part, and a controller configured to calculate an insertion path of the probe in the live body part based on the tracked movement of the probe during the insertion, and calculate a reinsertion path of the probe based on the calculated insertion path while compensating for the tracked movement of the live body part, and send control commands to the robotic arm to reinsert the probe in the live body part according to the calculated reinsertion path.

Systems and methods of operating power tools. The method includes receiving a command to start a recording mode at a first electronic processor of a first power tool, and receiving at the first electronic processor, a measured parameter from a sensor of the first power tool while a first motor of the first power tool is operating. The method also includes generating a recorded motor parameter by recording the measured parameter, on a first memory of the first power tool, when the first power tool operates in the recording mode, and transmitting, with a first transceiver of the first power tool, the recorded motor parameter. The method further includes receiving the recorded motor parameter at an external device, transmitting the recorded motor parameter to a second power tool via the external device, and receiving the recorded motor parameter via a second transceiver of the second power tool.

Systems and methods of operating power tools. The method includes receiving a command to start a recording mode at a first electronic processor of a first power tool, and receiving at the first electronic processor, a measured parameter from a sensor of the first power tool while a first motor of the first power tool is operating. The method also includes generating a recorded motor parameter by recording the measured parameter, on a first memory of the first power tool, when the first power tool operates in the recording mode, and transmitting, with a first transceiver of the first power tool, the recorded motor parameter. The method further includes receiving the recorded motor parameter at an external device, transmitting the recorded motor parameter to a second power tool via the external device, and receiving the recorded motor parameter via a second transceiver of the second power tool.

Techniques are described herein for generating a dynamical systems control policy. A non-parametric family of smooth maps is defined on which vector-field learning problems can be formulated and solved using convex optimization. In some implementations, techniques described herein address the problem of generating contracting vector fields for certifying stability of the dynamical systems arising in robotics applications, e.g., designing stable movement primitives. These learning problems may utilize a set of demonstration trajectories, one or more desired equilibria (e.g., a target point), and once or more statistics including at least an average velocity and average duration of the set of demonstration trajectories. The learned contracting vector fields may induce a contraction tube around a targeted trajectory for an end effector of the robot. In some implementations, the disclosed framework may use curl-free vector-valued Reproducing Kernel Hilbert Spaces.

Techniques are described herein for generating a dynamical systems control policy. A non-parametric family of smooth maps is defined on which vector-field learning problems can be formulated and solved using convex optimization. In some implementations, techniques described herein address the problem of generating contracting vector fields for certifying stability of the dynamical systems arising in robotics applications, e.g., designing stable movement primitives. These learning problems may utilize a set of demonstration trajectories, one or more desired equilibria (e.g., a target point), and once or more statistics including at least an average velocity and average duration of the set of demonstration trajectories. The learned contracting vector fields may induce a contraction tube around a targeted trajectory for an end effector of the robot. In some implementations, the disclosed framework may use curl-free vector-valued Reproducing Kernel Hilbert Spaces.

A robot controller includes a storage unit that stores load information including a mass and a center of gravity position of a load to be attached to a robot; a lead-through control unit that controls the robot comprising a sensor that detects an external force, based on the external force detected by the sensor and the load information stored in the storage unit; and a load suitability determining unit that determines whether or not the load information stored in the storage unit is suitable. In response to the load suitability determining unit determining that the load information has a possibility of being unsuitable, the lead-through control unit performs a restriction on a movement of the robot.

Provided are a robot system, a robot control device, a control method, and a program which make it possible to more simply teach a robot action. The robot system comprises: a feature point teaching unit which causes a storage unit to store the position of a feature point that has been taught using lead-through; an input accepting unit which accepts the input of an angle value of a tool with respect to a workpiece W; a posture determining unit which determines the posture of the tool on the basis of the angle value of the tool; and a program generating unit which generates a robot program for a robot on the basis of the position of the feature point and the posture.