Abstract: Implementations are described herein for offline computation and caching of precalculated joint trajectories. In various implementations, an instruction may be obtained to move an end effector of a robot between start and target positions. A first type of trajectory planning may be performed in real time or “online” to calculate a first joint trajectory of the robot that moves the end effector from the start to target position. The robot may then implement the first joint trajectory. A second type of trajectory planning may be performed offline, e.g., during downtime of the robot, to precalculate a second joint trajectory of the robot to move the end effector from the start to target position. The second type of trajectory planning may require more resources than were required by the first type of trajectory planning. Data indicative of the precalculated second joint trajectory of the robot may be stored for future use.

Abstract: In an embodiment, a mobile robotic device includes a mobile base and a mounting column fixed to the mobile base. The robotic device further includes a seven-degree-of-freedom (7DOF) robotic arm, including a rotatable joint that enables rotation of the 7DOF robotic arm relative to the mounting column. The robotic device additionally includes a perception housing comprising at least one sensor, where the mounting column, the rotatable joint of the 7DOF arm, and the perception housing are arranged in a stacked tower such that the rotatable joint of the 7DOF arm is above the mounting column and below the perception housing.

Abstract: Implementations are provided for improved robot reachability maps. Multiple reachability maps for a robot may be indexed on a distance of an end effector from a plane and a tilt of the end effector. Each reachability map may include multiple cells, each representing a base pose of the robot. A target pose of the end effector may be used to determine a target distance/tilt, which is used to select a given reachability map. A given cell of the given reachability map may be used to determine a first candidate set of joint parameters for the robot. In some implementations, one or more additional, neighboring cells of the plurality of cells may be used to determine additional candidate set(s) of joint parameters. A given set of joint parameters to be implemented to achieve the target pose of the end effector may be selected and used to operate the robot.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating and utilizing non-uniform volume measures for occupied voxels, where each of the occupied voxels represents an occupied point of an environment of a robot. The volume measure for each of the occupied voxels is a “padding” for the occupied voxel and indicates a volume to be utilized for that occupied voxel. The volume measures for the occupied voxels are non-uniform in that they are not all the same volume measure. During path planning, the non-uniform volume measures of the occupied voxels can be considered as “paddings” for the occupied voxels and the occupied voxels with their corresponding volume measures considered as obstacles.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating and utilizing non-uniform volume measures for occupied voxels, where each of the occupied voxels represents an occupied point of an environment of a robot. The volume measure for each of the occupied voxels is a “padding” for the occupied voxel and indicates a volume to be utilized for that occupied voxel. The volume measures for the occupied voxels are non-uniform in that they are not all the same volume measure. During path planning, the non-uniform volume measures of the occupied voxels can be considered as “paddings” for the occupied voxels and the occupied voxels with their corresponding volume measures considered as obstacles.

Abstract: Generating a grasp pose for grasping of an object by an end effector of a robot. An image that captures at least a portion of the object is provided to a user via a user interface output device of a computing device. The user may select one or more pixels in the image via a user interface input device of the computing device. The selected pixel(s) are utilized to select one or more particular 3D points that correspond to a surface of the object in the robot's environment. A grasp pose is determined based on the particular 3D points. For example, a local plane may be fit based on the particular 3D point(s) and a grasp pose determined based on a normal of the local plane. Control commands can be provided to cause the grasping end effector to be adjusted to the grasp pose, after which a grasp is attempted.

Abstract: Generating a grasp pose for grasping of an object by an end effector of a robot. An image that captures at least a portion of the object is provided to a user via a user interface output device of a computing device. The user may select one or more pixels in the image via a user interface input device of the computing device. The selected pixel(s) are utilized to select one or more particular 3D points that correspond to a surface of the object in the robot's environment. A grasp pose is determined based on the particular 3D points. For example, a local plane may be fit based on the particular 3D point(s) and a grasp pose determined based on a normal of the local plane. Control commands can be provided to cause the grasping end effector to be adjusted to the grasp pose, after which a grasp is attempted.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for determining one or more spatial constraints associated with an object to be acted upon by a robot; determining a plurality of candidate physical arrangements of the object that satisfy the one or more spatial constraints; calculating, for one or more of the plurality of candidate physical arrangements of the object, a candidate physical arrangement cost that would be incurred as a result of the robot acting upon the object in the candidate physical arrangement; and selecting, from the plurality of candidate physical arrangements, a candidate physical arrangement associated with a candidate physical arrangement cost that satisfies a criterion.

Abstract: Generating a grasp pose for grasping of an object by an end effector of a robot. An image that captures at least a portion of the object is provided to a user via a user interface output device of a computing device. The user may select one or more pixels in the image via a user interface input device of the computing device. The selected pixel(s) are utilized to select one or more particular 3D points that correspond to a surface of the object in the robot's environment. A grasp pose is determined based on the particular 3D points. For example, a local plane may be fit based on the particular 3D point(s) and a grasp pose determined based on a normal of the local plane. Control commands can be provided to cause the grasping end effector to be adjusted to the grasp pose, after which a grasp is attempted.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for selecting robot poses to account for cost. In various implementations, a plurality of candidate instruction sets may be determined. Each candidate instruction set may be configured to cause a robot to assume a different respective set of poses while traversing a reference point along a path. In various implementations, a cost incurred while the robot implements the candidate instruction set to traverse the reference point along the path may be calculated. A candidate instruction set associated with an incurred cost that satisfies a first criterion may be selected from the plurality of candidate instruction sets. In some implementations, the selected candidate instruction set and incurred cost may be associated with the path.