Abstract: 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.

Abstract: 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.

Abstract: A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.

Abstract: A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.

Abstract: A method for perception and fitting for a stair tracker includes receiving sensor data for a robot adjacent to a staircase. For each stair of the staircase, the method includes detecting, at a first time step, an edge of a respective stair of the staircase based on the sensor data. The method also includes determining whether the detected edge is a most likely step edge candidate by comparing the detected edge from the first time step to an alternative detected edge at a second time step, the second time step occurring after the first time step. When the detected edge is the most likely step edge candidate, the method includes defining, by the data processing hardware, a height of the respective stair based on sensor data height about the detected edge. The method also includes generating a staircase model including stairs with respective edges at the respective defined heights.

Abstract: A method for negotiating stairs includes receiving image data about a robot maneuvering in an environment with stairs. Here, the robot includes two or more legs. Prior to the robot traversing the stairs, for each stair, the method further includes determining a corresponding step region based on the received image data. The step region identifies a safe placement area on a corresponding stair for a distal end of a corresponding swing leg of the robot. Also prior to the robot traversing the stairs, the method includes shifting a weight distribution of the robot towards a front portion of the robot. When the robot traverses the stairs, the method further includes, for each stair, moving the distal end of the corresponding swing leg of the robot to a target step location where the target step location is within the corresponding step region of the stair.

Abstract: A method for negotiating stairs includes receiving image data about a robot maneuvering in an environment with stairs. Here, the robot includes two or more legs. Prior to the robot traversing the stairs, for each stair, the method further includes determining a corresponding step region based on the received image data. The step region identifies a safe placement area on a corresponding stair for a distal end of a corresponding swing leg of the robot. Also prior to the robot traversing the stairs, the method includes shifting a weight distribution of the robot towards a front portion of the robot. When the robot traverses the stairs, the method further includes, for each stair, moving the distal end of the corresponding swing leg of the robot to a target step location where the target step location is within the corresponding step region of the stair.

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 terrain and constraint planning a step plan includes receiving, at data processing hardware of a robot, image data of an environment about the robot from at least one image sensor. The robot includes a body and legs. The method also includes generating, by the data processing hardware, a body-obstacle map, a ground height map, and a step-obstacle map based on the image data and generating, by the data processing hardware, a body path for movement of the body of the robot while maneuvering in the environment based on the body-obstacle map. The method also includes generating, by the data processing hardware, a step path for the legs of the robot while maneuvering in the environment based on the body path, the body-obstacle map, the ground height map, and the step-obstacle map.

Abstract: A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.

Abstract: A legged robot may seek to operate according to a target gait. The legged robot may include leg members and leg joints. Possibly based on the target gait and state of the legged robot, an ordered list of gait controllers may be obtained. The gait controllers in the ordered list may define respective gaits of the legged robot, and may include respective validity checks and output parameters for the respective gaits. The ordered list may begin with a target gait controller that defines the target gait. The ordered list may be traversed in order from the target gait controller until a validity check associated with a particular gait controller passes. The legged robot may be instructed to actuate the leg members and/or leg joints according to output parameters of the particular gait controller.

Abstract: A method for terrain and constraint planning a step plan includes receiving, at data processing hardware of a robot, image data of an environment about the robot from at least one image sensor. The robot includes a body and legs. The method also includes generating, by the data processing hardware, a body-obstacle map, a ground height map, and a step-obstacle map based on the image data and generating, by the data processing hardware, a body path for movement of the body of the robot while maneuvering in the environment based on the body-obstacle map. The method also includes generating, by the data processing hardware, a step path for the legs of the robot while maneuvering in the environment based on the body path, the body-obstacle map, the ground height map, and the step-obstacle map.

Abstract: A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.

Abstract: A method for perception and fitting for a stair tracker includes receiving sensor data for a robot adjacent to a staircase. For each stair of the staircase, the method includes detecting, at a first time step, an edge of a respective stair of the staircase based on the sensor data. The method also includes determining whether the detected edge is a most likely step edge candidate by comparing the detected edge from the first time step to an alternative detected edge at a second time step, the second time step occurring after the first time step. When the detected edge is the most likely step edge candidate, the method includes defining, by the data processing hardware, a height of the respective stair based on sensor data height about the detected edge. The method also includes generating a staircase model including stairs with respective edges at the respective defined heights.

Abstract: 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.

Abstract: A method for negotiating stairs includes receiving image data about a robot maneuvering in an environment with stairs. Here, the robot includes two or more legs. Prior to the robot traversing the stairs, for each stair, the method further includes determining a corresponding step region based on the received image data. The step region identifies a safe placement area on a corresponding stair for a distal end of a corresponding swing leg of the robot. Also prior to the robot traversing the stairs, the method includes shifting a weight distribution of the robot towards a front portion of the robot. When the robot traverses the stairs, the method further includes, for each stair, moving the distal end of the corresponding swing leg of the robot to a target step location where the target step location is within the corresponding step region of the stair.

Abstract: 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.

Abstract: A legged robot may seek to operate according to a target gait. The legged robot may include leg members and leg joints. Possibly based on the target gait and state of the legged robot, an ordered list of gait controllers may be obtained. The gait controllers in the ordered list may define respective gaits of the legged robot, and may include respective validity checks and output parameters for the respective gaits. The ordered list may begin with a target gait controller that defines the target gait. The ordered list may be traversed in order from the target gait controller until a validity check associated with a particular gait controller passes. The legged robot may be instructed to actuate the leg members and/or leg joints according to output parameters of the particular gait controller.

Abstract: A method for negotiating stairs includes receiving image data about a robot maneuvering in an environment with stairs. Here, the robot includes two or more legs. Prior to the robot traversing the stairs, for each stair, the method further includes determining a corresponding step region based on the received image data. The step region identifies a safe placement area on a corresponding stair for a distal end of a corresponding swing leg of the robot. Also prior to the robot traversing the stairs, the method includes shifting a weight distribution of the robot towards a front portion of the robot. When the robot traverses the stairs, the method further includes, for each stair, moving the distal end of the corresponding swing leg of the robot to a target step location where the target step location is within the corresponding step region of the stair.