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.

A method for controlling a robot. The method includes providing demonstrations for performing each of a plurality of skills; training from the demonstrations, a robot trajectory model for each skill, each trajectory model is a hidden semi-Markov model having one or more initial states and one or more final states; training, from the demonstrations, a precondition model for each skill comprising, for each initial state, a probability distribution of robot configurations before executing the skill, and a final condition model for each skill comprising, for each final state, a probability distribution of robot configurations after executing the skill; receiving a description of a task, the task includes performing the skills of the plurality of skills in sequence and/or branches; generating a composed robot trajectory model; and controlling the robot according to the composed robot trajectory model to execute the task.

A device for and method of operating a machine. The method includes providing a sequence of skills of the machine for executing a task, selecting a sequence of states from a plurality of sequences of states, depending on a likelihood, wherein the likelihood is determined depending on a transition probability from a final state of a first sub-sequence of states of the sequence of states for a first skill in the sequence of skills to an initial state of a second sub-sequence of states of the sequence of states for a second skill in the sequence of skills.

A computer-implemented method of configuring a system which interacts with a physical environment. An action of the system in a state of the physical environment results in an updated state of the physical environment according to a transition probability. A safe set of state-action pairs known to be safely performable and an unsafe set of state-action pairs to be avoided are indicated. During an environment interaction, a safe set of state-action pairs is updated by estimating a transition probability for a state-action pair based on an empirical transition probability of a similar other state-action pair, and including the state-action pair in the safe set of state-action pairs only if the state-action pair is not labelled as unsafe and the safe set of state-action pairs can be reached with sufficient probability from the state-action pair based on the estimated transition probability.

The present disclosure relates to the intelligent distribution of data for robotic, autonomous, and similar systems. To reduce the impact of multi-agent coordination on networked systems embodiments are disclosed that include the use of action-based constraints which yield constrained-action POMDP (CA-POMDP) models, and probabilistic constraint satisfaction for the resulting infinite-horizon finite state controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite state controller (FSC). A combination of a Markov chain Monte Carlo (MCMC) routine and a discrete optimization routine can be performed on the finite state controller to improve probabilistic constraint satisfaction of the finite state controller, while minimizing impact to a value function.

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.

A controller for controlling robotic manipulator according to a task is provided. The controller is to collect data relating to a state and an object property of an object, and execute a state adapter model to produce a state correction to state of the object having the object property different from a unitary property of a unitary object. The controller is to execute a control policy using the state correction to produce an action for the unitary object, and execute an action adapter model to produce an action correction to the action produced by the control policy. The state correction and action correction are produced based on difference between object property and unitary property. The control policy is to map a state of the unitary object to the action of the robotic manipulator to manipulate the unitary object according to the task.

A controller for controlling robotic manipulator according to a task is provided. The controller is to collect data relating to a state and an object property of an object, and execute a state adapter model to produce a state correction to state of the object having the object property different from a unitary property of a unitary object. The controller is to execute a control policy using the state correction to produce an action for the unitary object, and execute an action adapter model to produce an action correction to the action produced by the control policy. The state correction and action correction are produced based on difference between object property and unitary property. The control policy is to map a state of the unitary object to the action of the robotic manipulator to manipulate the unitary object according to the task.

A method of controlling a robotic device. The method includes generating a robot control model for performing a task, wherein the robot control model comprises parameters which influence the performance of the task, adjusting the parameters of the robot control model by optimizing a target function which evaluates the adherence to at least one condition with respect to the temporal progression of at least one continuous sensor signal when performing the task, and controlling the robotic device according to the robot control model in order to perform the task using the adjusted parameters.