A machine learning device for a robot that allows a human and the robot to work cooperatively, the machine learning device including a state observation unit that observes a state variable representing a state of the robot during a period in that the human and the robot work cooperatively; a determination data obtaining unit that obtains determination data for at least one of a level of burden on the human and a working efficiency; and a learning unit that learns a training data set for setting an action of the robot, based on the state variable and the determination data.

A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

Methods and systems for enabling human-machine collaborations include a generalizable framework that supports dynamic adaptation and reuse of robotic capability representations and human-machine collaborative behaviors. Specifically, a method of feedback-enabled user-robot collaboration includes obtaining a robot capability that models a robot's functionality for performing task actions, specializing the robot capability with an information kernel that encapsulates task-related parameters associated with the task actions, and providing an instance of the specialized robot capability as a robot capability element that controls the robot's functionality based on the task-related parameters. The method also includes obtaining, based on the robot capability element's user interaction requirements, user interaction capability elements, via which the robot capability element receives user input and provides user feedback, controlling, based on the task-related parameters, the robot's functionality to perform the task actions in collaboration with the user input; and providing the user feedback including task-related information generated by the robot capability element in association with the task actions.

A machine learning device for a robot that allows a human and the robot to work cooperatively, the machine learning device including a state observation unit that observes a state variable representing a state of the robot during a period in that the human and the robot work cooperatively; a determination data obtaining unit that obtains determination data for at least one of a level of burden on the human and a working efficiency; and a learning unit that learns a training data set for setting an action of the robot, based on the state variable and the determination data.

Techniques for transferring highly dimensional movements to lower dimensional robot movements are described. In an example, a reference motion of a target is used to train a non-linear approximator of a robot to learn how to perform the motion. The robot and the target are associated with a robot model and a target model, respectively. Features related to the positions of the robot joints are input to the non-linear approximator. During the training, a robot joint is simulated, which results in movement of this joint and different directions of a robot link connected thereto. The robot link is mapped to a link of the target model. The directions of the robot link are compared to the direction of the target link to learn the best movement of the robot joint. The training is repeated for the different links and for different phases of the reference motion.

Embodiments of the disclosure provide a method and device for robot interactions. In one embodiment, a method comprises: collecting to-be-processed data reflecting an interaction output behavior; determining robot interaction output information corresponding to the to-be-processed data; controlling a robot to execute the robot interaction output information to imitate the interaction output behavior; collecting, in response to an imitation termination instruction triggered when the imitation succeeds, interaction trigger information corresponding to the robot interaction output information; and storing the interaction trigger information in relation to the robot interaction output information to generate an interaction rule.

A teaching device capable of teaching not only movement work but also more detailed working content. The teaching device is provided with input section for inputting work information such as work of pinching workpieces which is carried out by a robot arm at a working position. When carrying out motion capture by moving jig (an object which mimics the robot arm) which is provided with marker section, a user manipulate input section at an appropriate timing to input the working content to be performed by the robot arm as work information, and thus it is possible to set fine working content of the robot arm in teaching device. Accordingly, teaching device is capable of linking positional information of jig and the like and work information generating control information for controlling the robot arm.

The present disclosure provides a task management method for a robot, a robot using the same, and a computer readable storage medium. The method includes: obtaining a current task of the robot, in response to receiving a request for executing a new task of the robot; querying the preset state table according to the new task and the current task to determine whether to switch the robot from the current task to the new task: and switching the robot from the current task to the new task, in response to determining to switch. In this way, the stability of the operation of the robot can be improved, and the efficiency of the robot to execute tasks can be improved.

Higher demands for adaptable, scalable and automated human resources, capabilities and services, such as human well-being, safety and security, universal health care system and educational institutions, are growing in our societies, leading the way to the creation of a new artificially-intelligent support system that can facilitate the lives of the many. The current invention aims to help resolve such issues by offering a relief system that uses a new robotic specie, called Human-Robots (HRs) and that are intended to improve the quality of our lives in terms of education, health-care, well-being, safety and security. The new specie may autonomously work and move in close proximities with and among HBs and within their natural environments, to share their workloads and tasks and is especially tuned to their well-beings and of their surroundings. The robotic specie is reliant on a standard reconfigurable system and platform that can take multiple shapes, be modular, incremental, scalable, mobile, intelligent, connected, social, and possibly fully-autonomous. The new HR society will form the new race of autonomous personal and public service providers and take part in our society as a new specie.

A flexible robotic kitchen assistant automates the preparation of food items. The robotic kitchen assistant includes a robotic arm, a sensor assembly comprising a plurality of cameras aimed at a kitchen workspace for preparing the food items, a controller operable to move the robotic arm, and a processor. The processor is operable to command the robotic arm to perform a food preparation step on the food items in the kitchen workspace based on order information, recipe information, kitchen equipment information, and camera data. The system is capable of performing a wide range of tasks commonly used in residential and commercial kitchens and is able to work collaboratively with and in close proximity to human kitchen workers.