Abstract: Embodiments of the present disclosure are directed towards robotic systems and methods. The robot may include an end effector, a tool flange of the robot, and a joint. The end effector may include a contacting part configured to contact a workpiece. The joint may be positioned between, and connected to, the tool flange and the end effector. The joint may include a variable angle between the tool flange and the end effector.

Abstract: A method and computing system comprising identifying one or more candidate objects for selection by a robot. A path to the one or more candidate objects may be determined based upon, at least in part, a robotic environment and at least one robotic constraint. A feasibility of grasping a first candidate object of the one or more candidate objects may be validated. If the feasibility is validated, the robot may be controlled to physically select the first candidate object. If the feasibility is not validated, at least one of a different grasping point of the first candidate object, a second path, or a second candidate object may be selected.

Abstract: Embodiments of the present disclosure are directed towards a robotic system. The system may include a robot configured to receive an initial constrained approach for performing a robot task. The system may further include a graphical user interface in communication with the robot. The graphical user interface may be configured to allow a user to interact with the robot to determine an allowable range of robot poses associated with the robot task. The allowable range of robot poses may include fewer constraints than the initial constrained approach. The allowable range of poses may be based upon, at least in part, one or more degrees of symmetry associated with a workpiece associated with the robot task or an end effector associated with the robot. The system may also include a processor configured to communicate the allowable range of robot poses to the robot.

Abstract: A computing system is provided for training one or more machine learning models to perform at least a portion of a robotic task of a physical robotic system by monitoring a model-based control algorithm associated with the physical robotic system perform at least a portion of the robotic task. One or more robotic task predictions may be defined, via the one or more machine learning models, based upon, at least in part, the training of the one or more machine learning models. The one or more robotic task predictions may be provided to the model-based control algorithm associated with the physical robotic system. The robotic task may be performed, via the model-based control algorithm associated with the robotic system, on the physical robotic system based upon, at least in part, the one or more robotic task predictions defined by the one or more machine learning models.

Abstract: Embodiments of the present disclosure are directed towards robotic systems and methods. The robot may include an end effector, a tool flange of the robot, and a joint. The end effector may include a contacting part configured to contact a workpiece. The joint may be positioned between, and connected to, the tool flange and the end effector. The joint may include a variable angle between the tool flange and the end effector.

Abstract: A computing system is provided for training one or more machine learning models to perform at least a portion of a robotic task of a physical robotic system by monitoring a model-based control algorithm associated with the physical robotic system perform at least a portion of the robotic task. One or more robotic task predictions may be defined, via the one or more machine learning models, based upon, at least in part, the training of the one or more machine learning models. The one or more robotic task predictions may be provided to the model-based control algorithm associated with the physical robotic system. The robotic task may be performed, via the model-based control algorithm associated with the robotic system, on the physical robotic system based upon, at least in part, the one or more robotic task predictions defined by the one or more machine learning models.

Abstract: Embodiments of the present disclosure are directed towards a robotic system. The system may include a robot configured to receive an initial constrained approach for performing a robot task. The system may further include a graphical user interface in communication with the robot. The graphical user interface may be configured to allow a user to interact with the robot to determine an allowable range of robot poses associated with the robot task. The allowable range of robot poses may include fewer constraints than the initial constrained approach. The allowable range of poses may be based upon, at least in part, one or more degrees of symmetry associated with a workpiece associated with the robot task or an end effector associated with the robot. The system may also include a processor configured to communicate the allowable range of robot poses to the robot.

Abstract: A method and computing system comprising identifying one or more candidate objects for selection by a robot. A path to the one or more candidate objects may be determined based upon, at least in part, a robotic environment and at least one robotic constraint. A feasibility of grasping a first candidate object of the one or more candidate objects may be validated. If the feasibility is validated, the robot may be controlled to physically select the first candidate object. If the feasibility is not validated, at least one of a different grasping point of the first candidate object, a second path, or a second candidate object may be selected.