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.

The invention relates to a robot, a robot control system, and a method for controlling a robot. The robot comprises a movable, multi-membered robot structure (102) that can be driven by means of actuators (101), at least one marked structural element S being defined on the movable robot structure (102), with at least one point P.sub.S marked on the structural element S. The robot is designed such that, in an input mode, it learns positions POS.sub.PS of the point PS and/or poses of the structural element S in a work space of the robot, the user exerting an input force F.sub.EING on the movable robot structure in order to move the structural element S, which is conveyed to the point P.sub.S as F.sub.EING,PS, and/or to the structural element S as torque M.sub.EING,S. A control device (103) of the robot is designed such that, in the input mode, the actuators (101) are controlled on the basis of a pre-defined space-fixed virtual 3D grid that at least partially fills the work space, such that the structural element S is moved with a pre-defined force F.sub.GRID (POS.sub.PS), according to the current position POS.sub.PS of the point P.sub.S in the 3D grid, to the adjacent grid point of the 3D grid or in a grid point space defined around the adjacent grid point of the 3D grid, the point P.sub.S of the structural element S remaining on said adjacent grid point or in said grid point space in the event of the following holding true: |F.sub.EING,PS|<|F.sub.GRID(POS.sub.PS) and/or, in the input mode, the actuators (101) are controlled on the basis of a pre-defined virtual discrete 3D orientation space O, where the 3D orientation space O=: (.sub.i, .sub.j, .sub.k) where i=1, 2, . . . , I, j=1, 2, . . . J, k=1, 2, . . . , K is defined or can be defined by a pre-defined angle .sub.i, .sub.j, .sub.k, in such a way that the structural element S is moved with a pre-defined torque)(SO ROM according to the current orientation OR.sub.S of the structural element, towards the adjacent discrete orientation of the 3D orientation space O=: (.sub.i, .sub.j, .sub.k), S, the structural element remaining in said adjacent discrete orientation of the 3D orientation space O in the event that the following holds true: |M.sub.EING,S|<|M.sub.O(OR.sub.S).

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.

Devices, systems, and methods for providing a desired movement of one or more joints of a manipulator arm having a plurality of joints with redundant degrees of freedom while effecting commanded movement of a distal end effector of the manipulator. Methods include defining a constraint, such as a network of paths, within a joint space defined by the one or more joints and determining a movement of the plurality of joints within a null-space to track the constraints with the one or more joints. Methods may further include calculating a reconfiguration movement of the joints and modifying the constraints to coincide with a reconfigured position of the one or more joints. Various configurations for devices and systems utilizing such methods are provided herein.

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.

Devices, systems, and methods for providing a desired movement of one or more joints of a manipulator arm having a plurality of joints with redundant degrees of freedom while effecting commanded movement of a distal end effector of the manipulator. Methods include defining a constraint, such as a network of paths, within a joint space defined by the one or more joints and determining a movement of the plurality of joints within a null-space to track the constraints with the one or more joints. Methods may further include calculating a reconfiguration movement of the joints and modifying the constraints to coincide with a reconfigured position of the one or more joints. Various configurations for devices and systems utilizing such methods are provided herein.

Provided are a method, system, and non-transitory computer-readable medium for real-time autonomous path planning for a robotic device. The method includes receiving data about the robotic device in a three-dimensional workspace, encapsulating objects in the environment of the robotic device including the robotic device, a target, and one or more obstacles in simulated robotic space, calculating a direction of movement for the robotic device according to three virtual forces including a virtual attractive force, a virtual repulsive force, and a virtual tangential force acting at least partially perpendicularly relative to the virtual repulsive force, mapping each virtual force in the three-dimensional workspace into torque vectors in the simulated robotic space at each joint, converting a sum of the torque vectors into one or more commands for the robotic device defining a path to reach the target, and sending the one or more commands to the robotic device.

Devices, systems, and methods for providing a desired movement of one or more joints of a manipulator arm having a plurality of joints with redundant degrees of freedom while effecting commanded movement of a distal end effector of the manipulator. Methods include defining a constraint, such as a network of paths, within a joint space defined by the one or more joints and determining a movement of the plurality of joints within a null-space to track the constraints with the one or more joints. Methods may further include calculating a reconfiguration movement of the joints and modifying the constraints to coincide with a reconfigured position of the one or more joints. Various configurations for devices and systems utilizing such methods are provided herein.

The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.