Abstract: A method for learning a person movement model includes obtaining a data stream having time series of measured multi-dimensional movement data of the person from at least one sensor; segmenting the data into time segments, segment corresponding to one movement step; storing each segment into a first storage section with a predetermined memory size, performing clustering of the segments by computing distances between the segments, learning a two-dimensional non-linear topology preserving embedded feature vector based on the computed distances, clustering the vectors, aligning segments of each cluster and averaging the aligned segments of for generating a prototype segment for each cluster; generating and storing a movement model by comparing the determined number of clusters of a current clustering with the determined number of clusters from the previous clustering stored in a second storage section.

Abstract: Method for operating an assistance system based on an attribute space comprises: obtaining task information defining at least one target state to be achieved; obtaining initial state information defining an initial state of an autonomous device or a user, and of at least one object involved in achieving the target state; defining a planning problem based on the task information, the initial state information and attribute information related to the object; selecting an action or a sequence of actions to solve the planning problem; and outputting a control signal to a human-machine interface HMI or the autonomous device for achieving the target state; determining whether the action or sequence of actions achieves the target state, and, if not, decomposing the planning problem based on the attribute information to generate a decomposed planning problem; solving the decomposed planning problem; and outputting the control signal to the HMI or the autonomous device.

Abstract: The present invention provides a robot system for learning one or more new semantic relations in a knowledge base of physical skills. The system is configured to obtain knowledge on a physical skill by observing a demonstration, by a human user, of the physical skill, update the knowledge base with the obtained knowledge, generate a hypothesis of using the physical skill in a different context compared to a context of the demonstration by performing similarity considerations on the knowledge base using the obtained knowledge, present the hypothesis to the human user, receive a confirmation or a refusal from the human user with regard to the presentation of the hypothesis to the human user, and update the knowledge base according to the hypothesis in case of receiving the confirmation or discard the hypothesis in case of receiving the refusal.

Abstract: The present disclosure relates to a computer-implemented method for learning operators for planning a behavior of an autonomous device. The method includes obtaining a set of training data including observations of a plurality of skills executable by the autonomous device in an interaction with at least one entity for addressing a first task, obtaining a predetermined set of data defining a hierarchy of entities including the at least one entity, learning a set of individual skills based on the obtained set of training data, generating a set of generalized skills by generalizing the learned set of individual skills based on the predetermined set of data that defining a hierarchy of entities, performing behavior planning of the autonomous device for addressing a second task different from the first task based on the generalized set of skills.

Abstract: A method for controlling at least one effector trajectory of an effector of a robot for solving a predefined task is proposed. A sequence of postures are acquired to modify at least one of a contact constraint topology and an object constraint topology. A set of constraint equations are generated based on at least one of the modified contact constraint topology and the modified object constraint topology. On the generated set of constraint equations, a constraint relaxation is performed to generate a task description including a set of relaxed constraint equations. The at least one effector trajectory is generated by applying a trajectory generation algorithm on the task description. An inverse kinematics algorithm is performed to generate a control signal from the at least one effector trajectory. At least one effector is controlled to execute the at least one effector trajectory based on the generated control signal.

Abstract: A method and system for controlling at least one effector trajectory for at least one effector of a robot for solving a predefined task are proposed. A graph of postures is acquired, and at least one of a contact constraint topology and an object constraint topology are accordingly modified. A set of constraint equations based on at least one of the modified contact constraint topology and the modified object constraint topology are generated. Constraint relaxation is performed on the generated set of constraint equations to generate a task description including the relaxed set of constraint equations. The effector trajectory is generated by applying a trajectory generation algorithm on the generated task description. An inverse kinematics algorithm is performed on the generated effector trajectory for generating a control signal, and the effector is controlled to execute the effector trajectory based on the generated control signal.

Abstract: A method and simulation system for controlling at least one effector trajectory for solving a predefined task by at least one virtual effector. The method includes acquiring a sequence of postures to modify at least one of a contact constraint topology and an object constraint topology, generating a set of constraint equations based on at least one of the modified contact constraint topology and the modified object constraint topology, performing constraint relaxation on the generated set of constraint equations to generate a task description including the relaxed set of constraint equations, generating the effector trajectory by applying a trajectory generation algorithm on the generated task description, performing an inverse kinematics algorithm on the generated effector trajectory for generating a control signal, outputting the control signal to an output device, and generating, by the output device, image information displaying the effector trajectory of the virtual effector based on the control signal.

Abstract: A method for controlling at least one effector trajectory of an effector of a robot for solving a predefined task is proposed. A sequence of postures are acquired to modify at least one of a contact constraint topology and an object constraint topology. A set of constraint equations are generated based on at least one of the modified contact constraint topology and the modified object constraint topology. On the generated set of constraint equations, a constraint relaxation is performed to generate a task description including a set of relaxed constraint equations. The at least one effector trajectory is generated by applying a trajectory generation algorithm on the task description. An inverse kinematics algorithm is performed to generate a control signal from the at least one effector trajectory. At least one effector is controlled to execute the at least one effector trajectory based on the generated control signal.

Abstract: A method and system for controlling at least one effector trajectory for at least one effector of a robot for solving a predefined task are proposed. A graph of postures is acquired, and at least one of a contact constraint topology and an object constraint topology are accordingly modified. A set of constraint equations based on at least one of the modified contact constraint topology and the modified object constraint topology are generated. Constraint relaxation is performed on the generated set of constraint equations to generate a task description including the relaxed set of constraint equations. The effector trajectory is generated by applying a trajectory generation algorithm on the generated task description. An inverse kinematics algorithm is performed on the generated effector trajectory for generating a control signal, and the effector is controlled to execute the effector trajectory based on the generated control signal.

Abstract: A method and simulation system for controlling at least one effector trajectory for solving a predefined task by at least one virtual effector. The method includes acquiring a sequence of postures to modify at least one of a contact constraint topology and an object constraint topology, generating a set of constraint equations based on at least one of the modified contact constraint topology and the modified object constraint topology, performing constraint relaxation on the generated set of constraint equations to generate a task description including the relaxed set of constraint equations, generating the effector trajectory by applying a trajectory generation algorithm on the generated task description, performing an inverse kinematics algorithm on the generated effector trajectory for generating a control signal, outputting the control signal to an output device, and generating, by the output device, image information displaying the effector trajectory of the virtual effector based on the control signal.

Abstract: The disclosure regards a method and a system for assisting a person in assessing an environment. The method includes selecting a set of one or more entities present in the environment, obtaining coordinates in the environment and extents of each entity of the set, estimating a location and shape of each entity of the set on the basis of the obtained coordinates and extends of the respective entity, determining an area of interest on the basis of the estimated location and shape of each entity of the set, evaluating the area of interest with respect to one or more variables of interest in order to evaluate a deviation between a current state of the area of interest and a target state, and communicating the deviation from the target state to the person using coherent stimuli for stimulating the person's body.

Abstract: The disclosure regards a method and a system for assisting a person in assessing an environment. The method includes selecting a set of one or more entities present in the environment, obtaining coordinates in the environment and extents of each entity of the set, estimating a location and shape of each entity of the set on the basis of the obtained coordinates and extends of the respective entity, determining an area of interest on the basis of the estimated location and shape of each entity of the set, evaluating the area of interest with respect to one or more variables of interest in order to evaluate a deviation between a current state of the area of interest and a target state, and communicating the deviation from the target state to the person using coherent stimuli for stimulating the person's body.

Abstract: A support robot and control system having a support arm providing at least one degree of motion. The support robot includes a support arm supporting mechanism connected to the support arm and providing at least two degrees of motion. The support arm optionally includes a biomimetic actuator connectable to an end effector, a telescoping section operatively connected to the biomimetic actuator, and a tensile force transmitting member connected to an actuator. The actuator is capable of moving the biomimetic actuator towards and away from the actuator via the tensile force transmitting member. The support arm further includes an inflatable outer cover at least partially encasing the telescoping section and the tensile force transmitting member.

Abstract: A support robot and control system having a support arm providing at least one degree of motion. The support robot includes a support arm supporting mechanism connected to the support arm and providing at least two degrees of motion. The support arm optionally includes a biomimetic actuator connectable to an end effector, a telescoping section operatively connected to the biomimetic actuator, and a tensile force transmitting member connected to an actuator. The actuator is capable of moving the biomimetic actuator towards and away from the actuator via the tensile force transmitting member. The support arm further includes an inflatable outer cover at least partially encasing the telescoping section and the tensile force transmitting member.

Abstract: The invention relates to a method for improving operation of at least one robot. The robot is being operated on the basis of a set of predefined actions. A method comprises generating combined actions by combining at least two actions out of a set of original actions stored in an action library. Storing the combined actions in the actions library in addition to the original actions. Applying a reinforcement learning algorithm to the set of actions stored now in the action library to learn a control policy making use of the original actions and the combined actions. And finally, operating the robot on the basis of the resulting action library.

Abstract: The invention relates to a method for improving operation of at least one robot. The robot is being operated on the basis of a set of predefined actions. A method comprises generating combined actions by combining at least two actions out of a set of original actions stored in an action library. Storing the combined actions in the actions library in addition to the original actions. Applying a reinforcement learning algorithm to the set of actions stored now in the action library to learn a control policy making use of the original actions and the combined actions. And finally, operating the robot on the basis of the resulting action library.

Abstract: A method for controlling behavior of an intelligent mechanical system having more than one degree of freedom. The method includes (a) providing a target to a selector unit that requires operation of actuators of the mechanical system; (b) generating by a selector unit more than one behavior commands adapted to reach the target; (c) simulating movements of the mechanical system for each behavior command by a computing unit of the mechanical system; (d) assessing fitness values for each simulated movements based on at least one objective; and (e) sending the behavior commands with a highest fitness value to the actuators of the mechanical system.

Abstract: The invention proposes a method for imitation-learning of movements of a robot, wherein the robot performs the following steps: observing a movement of an entity in the robot's environment, recording the observed movement using a sensorial data stream and representing the recorded movement in a different task space representations, selecting a subset of the task space representations for the imitation learning and reproduction of the movement to be imitated.

Abstract: The invention refers to a method for controlling the effector trajectory from a current state to a target state. First invariant control parameters of the trajectory are determined. The effector trajectory is then represented in a task description being void of the invariant control parameters. The effector trajectory is controlled on the basis of this task description. The invention further refers to a method for controlling the effector trajectory wherein the effector trajectory is calculated by mapping increments from a control parameter space on a configuration space. The dimensional difference between the configuration space and the control parameter space leaves redundant degrees of freedom of a Null space. The degrees of freedom of the Null space are increased using a task description being void of invariant control parameters.

Abstract: A robot is provided with a motion control unit that avoids collision between segments of the robot or between segments of the robot and other objects. The motion control unit of the robot comprises a distance computing module, a whole body control module, a collision avoidance module, and a blending control unit. The distance computing module calculates two closest points of different segments of the robot connected to each other via at least one joint or a segment of the robot and another object. The collision avoidance module is provided with the information about the two closest points. The blending control unit combines the weighted output control signals of the whole body control module and the collision avoidance control module. The weight of the whole body control output signal is higher when the risk of collision is lower. The weight of the collision avoidance control output signal is higher when the risk of collision is higher.