Computer-implemented method for planning a manipulation task of an agent, particularly a robot, comprising the steps of: - Learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated, wherein the learning of the number of manipulation skills is performed in that a plurality of manipulation trajectories for each respective manipulation skill is recorded, a task parametrized Hidden Semi-Markov model TP-HSMM is determined depending on the plurality of manipulation trajectories for each respective manipulation skill and the symbolic abstraction of the respective manipulation skill is generated by constructing a PDDL model, wherein objects, initial state and goal specification define a problem instance, while a set of predicates and actions define a domain of a given manipulation; - Determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction such that the probability of achieving a given goal specification indicating a given complex manipulation task is maximized wherein a PDDL planning step is used to find a sequence of manipulation skills to fulfill the given goal specification, starting from a given initial state; wherein determining the concatenated sequence of manipulation skills includes the steps of: - a) Cascading the TP-HSMMs of consecutive manipulation skills into one complete model by computing transition probabilities according to a divergence of emission probabilities between end states and initial states of consecutive manipulations skills; - b) Searching a most-likely complete state sequence between the initial and goal states of the manipulation task using a modified Viterbi algorithm, wherein the modified Viterbi algorithm includes missing observations and duration probabilities; - c) Generating a robot end-effector trajectory that optimally tracks the most-likely complete state sequence; and - Executing the sequence of manipulation skills. - Learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated, wherein the learning of the number of manipulation skills is performed in that a plurality of manipulation trajectories for each respective manipulation skill is recorded, a task parametrized Hidden Semi-Markov model TP-HSMM is determined depending on the plurality of manipulation trajectories for each respective manipulation skill and the symbolic abstraction of the respective manipulation skill is generated by constructing a PDDL model, wherein objects, initial state and goal specification define a problem instance, while a set of predicates and actions define a domain of a given manipulation; - Determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction such that the probability of achieving a given goal specification indicating a given complex manipulation task is maximized wherein a PDDL planning step is used to find a sequence of manipulation skills to fulfill the given goal specification, starting from a given initial state; wherein determining the concatenated sequence of manipulation skills includes the steps of: - a) Cascading the TP-HSMMs of consecutive manipulation skills into one complete model by computing transition probabilities according to a divergence of emission probabilities between end states and initial states of consecutive manipulations skills; - b) Searching a most-likely complete state sequence between the initial and goal states of the manipulation task using a modified Viterbi algorithm, wherein the modified Viterbi algorithm includes missing observations and duration probabilities; - c) Generating a robot end-effector trajectory that optimally tracks the most-likely complete state sequence; and - Executing the sequence of manipulation skills.