Boston Dynamics, Inc.

A method includes receiving sensor data of an environment about a robot and generating a plurality of waypoints and a plurality of edges each connecting a pair of the waypoints. The method includes receiving a target destination for the robot to navigate to and determining a route specification based on waypoints and corresponding edges for the robot to follow for navigating the robot to the target destination selected from waypoints and edges previously generated. For each waypoint, the method includes generating a goal region encompassing the corresponding waypoint and generating at least one constraint region encompassing a goal region. The at least one constraint region establishes boundaries for the robot to remain within while traversing toward the target destination. The method includes navigating the robot to the target destination by traversing the robot through each goal region while maintaining the robot within the at least one constraint region.

Systems and methods are described for detecting changes at a location based on image data by a mobile robot. A system can instruct navigation of the mobile robot to a location. For example, the system can instruct navigation to the location as part of an inspection mission. The system can obtain input identifying a change detection. Based on the change detection and obtained image data associated with the location, the system can perform the change detection and detect a change associated with the location. For example, the system can perform the change detection based on one or more regions of interest of the obtained image data. Based on the detected change and a reference model, the system can determine presence of an anomaly condition in the obtained image data.

Systems and methods are described for combining sensor data obtained by a mobile robot. A system can obtain first sensor data from one or more first sensors of a robot. The system can determine a distance between the robot and at least a portion of the environment based on the first sensor data. For example, the distance may be a depth from a depth map. The system can obtain second sensor data from one or more second sensors of the robot. The system can combine a first portion of the second sensor data and a second portion of the second sensor data based on the distance. For example, the system can use the distance to determine a seam for combination of the first image and the second image. The system can instruct output of a user interface based on the combination.

Systems and methods are described for mapping and traversal of a set of stairs. A system can obtain a map of an environment. The system can determine that the environment includes a set of stairs based on the map. For example, the map may indicate the presence of the set of stairs within the environment. For mapping the set of stairs, the system can instruct performance of a stair mapping maneuver based on the determination that the environment includes the set of stairs. Based on the performance of the stair mapping maneuver, the system can map the set of stairs. The system can instruct traversal of the set of stairs by a robot based on the mapping of the set of stairs.

Systems and methods are described for docking a legged robot. A system can obtain sensor data from one or more sensors of the legged robot. The system can obtain dock association data associated with the legged robot. The dock association data may indicate an association of two or more docks to the legged robot. Based on the dock association data, the system can identify a dock of the two or more docks. The system can instruct the legged robot to perform a docking maneuver relative to the dock based on the sensor data and based on identifying the dock. For example, the system can instruct the legged robot to dock at the dock.

Systems and methods are described for provision of alerts. A system can obtain sensor data associated with an environment. The system can obtain an input indicating one or more filter parameters, one or more requests, and/or one or more alert parameters. For example, the input may indicate a request, a region of the environment, a region of sensor data, etc. The system can filter the sensor data based on the one or more filter parameters to obtain a filtered portion of the sensor data. The system may generate a prompt for a machine learning model that includes the filtered portion of the sensor data, the one or more requests, and the one or more alert parameters. The system can provide the prompt to a computing system. The system can obtain a output from the computing system and can provide an alert based on the output.

Systems and methods for semantic robot hazard avoidance with multi-modal prompting are provided. In one aspect, a method includes receiving a user input indicative of one or more hazards in an environment of the robot and image data indicative of the one or more hazards in the environment. The method also includes generating one or more segments of the image data. Each of the one or more segments corresponds to at least one of the one or more hazards indicated by the user input. The method further includes identifying a semantic label for each of the one or more segments, generating a hazard map including a location of each of the one or more segments and the corresponding semantic label, and navigating the robot through the environment based at least in part on the hazard map.

An example implementation involves controlling robots with non-constant body pitch and height. The implementation involves obtaining a model of the robot that represents the robot as a first point mass rigidly coupled with a second point mass along a longitudinal axis. The implementation also involves determining a state of a first pair of legs, and determining a height of the first point mass based on the model and the state of the first pair of legs. The implementation further involves determining a first amount of vertical force for at least one leg of the first pair of legs to apply along a vertical axis against a surface while the at least one leg is in contact with the surface. Additionally, the implementation involves causing the at least one leg of the first pair of legs to begin applying the amount of vertical force against the surface.

A method of grasping and/or placing multiple objects by a gripper of a mobile robot. The multi-grasp method includes determining one or more candidate groups of objects to grasp by the suction-based gripper of the mobile robot, each of the one or more candidate groups of objects including a plurality of objects, determining a grasp quality score for each of the one or more candidate groups of objects, and grasping, by the suction-based gripper of the mobile robot, all objects in a candidate group of objects based, at least in part, on the grasp quality score. The multi-place method includes determining an allowed width associated with the conveyor, selecting a multi-place technique based, at least in part, on the allowed width and a dimension of the multiple grasped objects, and controlling the mobile robot to place the multiple grasped objects on the conveyor based on the selected multi-place technique.