A robotic system is integrated with one or more mobile computing devices. Physical configurations of individual components of the system in physical space, or agents, under control of a user or users, are duplicated in a representation in virtual space. Some degree of real-time parity is maintained between the physical and virtual spaces, so as to implement a virtual environment that mirrors the physical one. Events occurring within one environment can directly influence and bear consequence on the course of events occurring within the other environment. Elements of virtual space thereby become truly interdependent and unified on a peer footing with elements in physical space. In at least one embodiment, the system is implemented as an application in entertainment, such as the manifestation of a video game in physical space.

Teams of robots can be organized to collectively complete complex real-world tasks, for example collective foraging in which robots search for, pick up, and drop off targets in a collection zone. A dynamic multiple-place foraging algorithm (MPFAdynamic) is a scalable, flexible, and efficient algorithm for robot swarms to collect objects in unmapped environments. It achieves scalability through a decentralized architecture in which robots search without central control, and then return to mobile depots which provide collection and communication points. Mobile depots move closer to clusters of targets as robots discover them, which reduces robot transport time as well as collisions among robots. Flexibility is achieved by incorporating individual robot behaviors in which robots move and communicate in ways that mimic the foraging behaviors of ants. The MPFAdynamic algorithm demonstrates that dispersed agents that dynamically adapt to local information in their environment provide more flexible and scalable swarms.

A method and apparatus for controlling and coordinating a multi-component system. Each component in the system contains a computing device. Each computing device is controlled by software running on the computing device. A first portion of the software resident on each computing device is used to control operations needed to coordinate the activities of all the components in the system. This first portion is known as a coordinating process. A second portion of the software resident on each computing device is used to control local processes (local activities) specific to that component. Each component in the system is capable of hosting and running the coordinating process. The coordinating process continually cycles from component to component while it is running. The continuous cycling of the coordinating process presents the programmer with a virtual machine in which there is a single coordinating process operating with a global view although, in fact, the data and computation remain distributed across every component in the system.

A robot control apparatus that performs communication with a plurality of robots R according to a communication environment includes: a wireless communication environment map creation unit that collects communication environment data transmitted from the plurality of robots and creates a wireless communication environment map; a service determination unit that determines change in services to be performed by the plurality of robots on the basis of the created wireless communication environment map; and a communication interface that performs communication with the plurality of robots using the wireless communication environment such as a wireless communication access point. The service determination unit determines a change process of dispositions of the plurality of robots, a stop process and a restoration process of functions thereof, or a change process of the service scenarios thereof on the basis of the wireless communication environment map.

A method for operating a robotic agent swarm system. The method includes: sending, via a communication network, a reconfiguration instruction from an orchestration controller to a number of robotic luminaire agents, each of the robotic luminaire agents of the swarm system being held at least periodically against an architectural surface comprising a holonomic operational area by a suspension and having a light source configured to illuminate a region in a proximity of the architectural surface. The method also includes changing one or more operating conditions of one or more of the robotic luminaire agents in response to the reconfiguration instruction, including holonomically moving at least one of the robotic luminaire agents from a first position on the holonomic operational area to a second position on the holonomic operational area.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, one or more computers receive object classification examples from a plurality of robots. Each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding. The object classification examples are evaluated based on a similarity of the received embeddings with respect to other embeddings. A subset of the object classification examples is selected based on the evaluation of the quality of the embeddings. The subset of the object classification examples is distributed to the robots in the plurality of robots.

Techniques are provided for discovery and monitoring of an environment using a plurality of robots. A plurality of robots navigate an environment by determining a navigation buffer for each of the robots; and allowing each of the robots to navigate within the environment while maintaining a substantially minimum distance from other robots, wherein the substantially minimum distance corresponds to the navigation buffer, and wherein a size of each of the navigation buffers is reduced over time based on a percentage of the environment that remains to be navigated. The robots can also navigate an environment by obtaining a discretization of the environment to a plurality of discrete regions; and determining a next unvisited discrete region for one of the plurality of robots to explore in the exemplary environment using a breadth-first search. The plurality of discrete regions can be, for example, a plurality of real or virtual tiles.

There is provided a control device, a control method, and a control system that implement a robot that flexibly executes a task in cooperation with another robot, the control device including: an ability management unit that determines capability indicating ability that can be executed by a first robot at predetermined timing as of that timing; a help management unit that compares ability required for a task to be executed by the first robot with the capability of the first robot and generates a help list indicating ability required for execution of the task; and a cooperation management unit that instructs a second robot having the capability that satisfies the ability indicated in the help list to execute the task in cooperation with the first robot.

A method for operating a multi-agent system having a plurality of robots. Each of the robots execute the following method cyclically until a target system state is achieved: starting from an instantaneous system state, determining possible options where progress is made along a path of system states in a predefined, deterministic finite automaton; the options defining actions through which a transition from a current to a subsequent system state can be achieved; determining a cost value for each of the possible options to carry out an action specified by the option; performing an auction, the cost values ascertained for each option being considered by each of the remaining robots; and executing an action, which corresponds to one of the options, as a function of all of the cost values which are determined or received for the respective option.

A method and apparatus for controlling and coordinating a multi-component system. Each component in the system contains a computing device. Each computing device is controlled by software running on the computing device. A first portion of the software resident on each computing device is used to control operations needed to coordinate the activities of all the components in the system. This first portion is known as a coordinating process. A second portion of the software resident on each computing devised is used to control local processes (local activities) specific to that component. Each component in the system is capable of hosting and running the coordinating process. The coordinating process continually cycles from component to component while it is running. The continuous cycling of the coordinating process presents the programmer with a virtual machine in which there is a single coordinating process operating with a global view although, in fact, the data and computation remain distributed across every component in the system.