Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

Abstract: A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

Abstract: A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels based on the at least one original set of sensor data. The plurality of voxels includes at least one ground voxel and at least one obstacle voxel. The method also includes generating a spherical depth map based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.

Abstract: A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels based on the at least one original set of sensor data. The plurality of voxels includes at least one ground voxel and at least one obstacle voxel. The method also includes generating a spherical depth map based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method of constrained mobility mapping includes receiving from at least one sensor of a robot at least one original set of sensor data and a current set of sensor data. Here, each of the at least one original set of sensor data and the current set of sensor data corresponds to an environment about the robot. The method further includes generating a voxel map including a plurality of voxels based on the at least one original set of sensor data. The plurality of voxels includes at least one ground voxel and at least one obstacle voxel. The method also includes generating a spherical depth map based on the current set of sensor data and determining that a change has occurred to an obstacle represented by the voxel map based on a comparison between the voxel map and the spherical depth map. The method additional includes updating the voxel map to reflect the change to the obstacle.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for downloading targeted object recognition modules that are selected from a library of candidate targeted object recognition modules based on various signals. In some implementations, an object recognition client may be operated to facilitate object recognition for a robot. It may download targeted object recognition module(s). Each targeted object recognition module may facilitate inference of an object type or pose of an observed object. The targeted object module(s) may be selected from a library of targeted object recognition modules based on various signals, such as a task to be performed by the robot. The object recognition client may obtain vision data capturing at least a portion of an environment in which the robot operates. The object recognition client may determine, based on the vision data and the downloaded object recognition module(s), information about an observed object in the environment.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for downloading targeted object recognition modules that are selected from a library of candidate targeted object recognition modules based on various signals. In some implementations, an object recognition client may be operated to facilitate object recognition for a robot. It may download targeted object recognition module(s). Each targeted object recognition module may facilitate inference of an object type or pose of an observed object. The targeted object module(s) may be selected from a library of targeted object recognition modules based on various signals, such as a task to be performed by the robot. The object recognition client may obtain vision data capturing at least a portion of an environment in which the robot operates. The object recognition client may determine, based on the vision data and the downloaded object recognition module(s), information about an observed object in the environment.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for downloading targeted object recognition modules that are selected from a library of candidate targeted object recognition modules based on various signals. In some implementations, an object recognition client may be operated to facilitate object recognition for a robot. It may download targeted object recognition module(s). Each targeted object recognition module may facilitate inference of an object type or pose of an observed object. The targeted object module(s) may be selected from a library of targeted object recognition modules based on various signals, such as a task to be performed by the robot. The object recognition client may obtain vision data capturing at least a portion of an environment in which the robot operates. The object recognition client may determine, based on the vision data and the downloaded object recognition module(s), information about an observed object in the environment.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for efficient mapping of a robot environment. In various implementations, a group of data points may be sensed by a three-dimensional sensor. One or more voxels of a three-dimensional voxel model that are occupied by the group of data points may be identified. For each occupied voxel, a column of the three-dimensional voxel model that contains the occupied voxel may be identified. Occupied voxels contained in each column may be indexed by elevation. In various implementations, one or more sparse linked data structures may be used to represent the columns.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for downloading targeted object recognition modules that are selected from a library of candidate targeted object recognition modules based on various signals. In some implementations, an object recognition client may be operated to facilitate object recognition for a robot. It may download targeted object recognition module(s). Each targeted object recognition module may facilitate inference of an object type or pose of an observed object. The targeted object module(s) may be selected from a library of targeted object recognition modules based on various signals, such as a task to be performed by the robot. The object recognition client may obtain vision data capturing at least a portion of an environment in which the robot operates. The object recognition client may determine, based on the vision data and the downloaded object recognition module(s), information about an observed object in the environment.