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.

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

Devices, systems, and methods for providing increased range of movement of the end effector of a manipulator arm having a plurality of joints with redundant degrees of freedom. Methods include defining a position-based constraint within a joint space defined by the at least one joint, determining a movement of the joints along the constraint within a null-space and driving the joints according to a calculated movement to effect the commanded movement while providing an increased end effector range of movement, particularly as one or more joints approach a respective joint limit within the joint space.

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

The present disclosure relates to robot path planning. Depth information of a plurality of obstacles in an environment of a robot are obtained at a first time instance. A static distance map is generated based on the depth information. A path is computed for the robot based on the static distance map. At a second time instant, depth information of one or more obstacles is obtained. A dynamic distance map is generated based on the one or more obstacles, wherein for each obstacle that satisfies a condition: a vibration range of the obstacle is computed based on a position of the obstacle and the static distance map, and the obstacle is classified as a dynamic obstacle or a static obstacle based on a criterion associated with the vibration range. A repulsive speed of the robot is computed based on the dynamic distance map to avoid the dynamic obstacles.

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 present disclosure relates to robot path planning. Depth information of a plurality of obstacles in an environment of a robot are obtained at a first time instance. A static distance map is generated based on the depth information. A path is computed for the robot based on the static distance map. At a second time instant, depth information of one or more obstacles is obtained. A dynamic distance map is generated based on the one or more obstacles, wherein for each obstacle that satisfies a condition: a vibration range of the obstacle is computed based on a position of the obstacle and the static distance map, and the obstacle is classified as a dynamic obstacle or a static obstacle based on a criterion associated with the vibration range. A repulsive speed of the robot is computed based on the dynamic distance map to avoid the dynamic obstacles.

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=: (α.sub.i, β.sub.j, γ.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 α.sub.i, β.sub.j, γ.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=: (α.sub.i, β.sub.j, γ.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.

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.