X Development LLC

Google Inc.

Gdm Holding LLC

Elmar Mair

Jonas Witt

Yunfei Bai

Justine Rembisz

Yuchen Wu

Vincent Nabat

Max Moroz

Weidan Wu

Ian Wilkes

James Lubin

John Tran

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

Mitigating reality gap through optimization of simulated hardware parameter(s) of simulated robot

Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

Semantic mapping of environments for autonomous devices

A mobile robotic device includes a mobile base and a mast fixed relative to the mobile base. The mast includes a carved-out portion. The mobile robotic device further includes a three-dimensional (3D) lidar sensor mounted in the carved-out portion of the mast and fixed relative to the mast such that a vertical field of view of the 3D lidar sensor is angled downward toward an are in front of the mobile robotic device.

Robot configuration with three-dimensional lidar

A docking station is provided that includes at least one component configured to couple to a robot and an identification surface. The identification surface includes a first curvature that varies at a first substantially constant rate of change along a first dimension the identification includes a second curvature that varies at a second substantially constant rate of change along a second dimension. The second dimension is orthogonal to the first dimension. The identification surface includes a third curvature that varies at a third substantially constant rate of change along a third dimension. The third dimension is orthogonal to the first dimension and the second dimension.

Robot docking station identification surface

Methods, systems, and apparatus for receiving a mapping of a property that includes a three-dimensional representation of the property, receiving observations of the property that each depict a portion of the property, providing the mapping and the observations to an object mapping engine, receiving an object mapping of the property, wherein the object mapping includes a plurality of object labels that each identify an object that was recognized from the observations and a location of the object within the three-dimensional representation that corresponds to a physical location of the object in the property, and obtaining a semantic mapping of the property that identifies semantic zones of the property with respect to the three-dimensional representation, wherein the semantic mapping is generated based on an output that results from a semantic mapping model processing the object mapping.

Semantic zone separation for map generation

Methods, systems, and apparatus for receiving data that represents a portion of a property that was obtained by a robot, identifying, based at least on the data, objects that the data indicates as being located within the portion of the property, determining, based on the objects, a semantic zone type corresponding to the portion of the property, accessing a mapping hierarchy for the property, wherein the mapping hierarchy for the property specifies semantic zones of the property that have corresponding semantic zone types and are associated with locations at the property, and specifies characteristics of the semantic zones, and selecting, from among the semantic zones and based at least on the semantic zone type and the data, a particular semantic zone, and setting, as a current location of the robot at the property, a particular location at the property associated with the particular semantic zone.

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Santa Cruz, CA, United States of America

Elmar Mair