A method for controlling a robot is provided. The method includes the steps of: determining a target robot to travel to a first loading station among a plurality of robots, on the basis of information on a location of the first loading station and a task situation of each of the plurality of robots, when a first transport target object is placed at the first loading station; and determining a travel route of the target robot with reference to information on the location of the first loading station and a location of a first unloading station associated with the first transport target object.

Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for state estimation in a robotics system. One of the systems includes an execution subsystem configured to drive one or more robots in an operating environment including continually evaluating a plurality of execution predicates, wherein each execution predicate comprises a rule having a predicate value, and wherein, whenever a state value that satisfies the predicate value of the predicate is detected by the execution subsystem, the execution subsystem is configured to trigger a corresponding action to be performed in the operating environment by the one or more robots. A state estimator is configured to continually execute a state estimation function using one or more sensor values or status messages obtained from the operating environment and to automatically update a discrete state value for a first execution predicate of the plurality of execution predicates evaluated by the execution subsystem.

A computer-implemented method includes obtaining a collection of object models for a plurality of different types of objects belonging to a same object category, generating a canonical representation for objects belonging to the object category, performing a plurality of downstream tasks using a plurality of different robot grasps on instances of objects belonging to the category and evaluating each grasp according to success or failure of the downstream task; and generating one or more category-level grasping areas for the canonical representation for objects belonging to the object category including aggregating the evaluations of grasps according to the downstream task.

A robotic system is disclosed. The system includes a communication interface that receives, from one or more sensors deployed in a workspace, sensor data indicative of a current state of the workspace. The system includes one or more processors that use the sensor data to estimate a state of one or both of the pallet or other receptacle and the set of zero or more items stacked on or in the receptacle, and use the estimated state to generate or update a plan to control a robotic arm to place a next set of items on or in, or remove the next set of items from, the pallet or other receptacle, the plan comprising an ordered sequence of item placements or removals. The plan is generated or updated based at least in part by performing a bounded tree search in which a subset of possible ordered sequences is explored.

A computer-implemented method comprising, receiving data representing a successful trajectory for an insertion task using a robot to insert a connector into a receptacle, performing a parameter optimization process for the robot to perform the insertion task. This parameter optimization includes defining an objective function that measures a similarity of a current trajectory generated with a current set of parameters to the successful trajectory and repeatedly modifying the current set of parameters and evaluating the modified set of parameters according to the objective function until generating a final set of parameters.

A robotic system is disclosed. The system includes a communication interface that receives, from a sensor(s) deployed in a workspace, sensor data indicative of a current state of the workspace, the workspace comprising a pallet or other receptacle and a plurality of items stacked on or in the receptacle. The system includes one or more processors that control a robotic arm to place a first set of items on or in, or remove the first set of items from, the pallet or other receptacle, update a geometric model based on the first set of items placed on or in a receptacle, use the geometric model in combination with the sensor data to estimate a stack of one or more items on or in the receptacle, and use the estimated state to generate or update a plan to control the robotic arm to place a second set of items.

An electronic device is provided. The electronic device includes a memory and a processor configured to move the electronic device, obtain an image and location of the external electronic device, identify the external electronic device based on the obtained image, transmit the plurality of control commands to the identified external electronic device, monitor a response of the external electronic device, store an identifier of a first control command set and the location of the external electronic device, wherein the first control command is based on the monitoring, and the first control command set includes the first control command that performs a pre-specified first operation, receive a first input, move the electronic device based on the received first input and the location, and transmit the first control command to the external electronic device based on the received first input and the identifier of the first control command set.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that distributes skill bundles that can guide robot execution. One of the methods includes receiving data for a skill bundle from a skill developer. The data can include a definition of one or more preconditions for a robotic system to execute a skill; one or more effects to an operating environment after the robotic system has executed the skill; and a software module implementing the skill. The software module can define a state machine of subtasks. A skill bundle can be generated from the data received from the skill developer. Data identifying the generated skill bundle can be added to a skill registry. The skill bundle can be provided to the execution robot system for installation on the robot execution system.