Modern robotic applications pose complex requirements with respect to the adaptation of

actions regarding the variability in a given task. Reinforcement learning can optimize for

changing conditions, but relearning from scratch is hardly feasible due to the high number of

required rollouts. This work proposes a parameterized skill that generalizes to new actions

for changing task parameters. The actions are encoded by a meta-learner that provides

parameters for task-specific dynamic motion primitives. Experimental evaluation shows that

the utilization of parameterized skills for initialization of the optimization process leads to a

more effective incremental task learning. A proposed hybrid optimization method combines

a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter

search in the unrestricted space of actions. It is shown that the developed algorithm reduces

the number of required rollouts for adaptation to new task conditions. Further, this work

presents a transfer learning approach for adaptation of learned skills to new situations.

Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point

reaching task and parameterized drumming on a pneumatic robot validate the approach.

But parameterized skills that are applied on complex robotic systems pose further

challenges: the dynamics of the robot and the interaction with the environment introduce

model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic

systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of

the complete dynamics model is not feasible due to the high complexity, this thesis examines

two alternative approaches: First, an improvement of the low-level control based on an

equilibrium model of the robot. Utilization of an equilibrium model reduces the learning

complexity and this thesis evaluates its applicability for control of pneumatic and industrial

light-weight robots. Second, an extension of parameterized skills to generalize for forward

signals of action primitives that result in an enhanced control quality of complex robotic

systems. This thesis argues for a shift in the complexity of learning the full dynamics of the

robot to a lower dimensional task-related learning problem. Due to the generalization in

relation to the task variability, online learning for complex robots as well as complex scenarios

becomes feasible. An experimental evaluation investigates the generalization capabilities of

the proposed online learning system for robot motion generation. Evaluation is performed

through simulation of a compliant 2-DOF arm and scalability to a complex robotic system

is demonstrated for a pneumatically driven humanoid robot with 8-DOF.