Recently, it has been possible to observe the acceleration of robot deployment in domains beyondthe usual industrial and manufacturing framework. However, for the majority of autonomoustasks, the definition of an analytical model or the search for an optimal (or acceptable) solutionrequires resources that are seldom available in real-time, thus favoring learning-based techniques.Indeed, learned models present the advantage of being model-free as well as having a constantexecution time, consequently enabling the realization of highly complex trajectories and tasks.Data-driven techniques, however, are hindered by considerable training time, frequently requiringmillions of examples and interactions with their environment to build acceptable control policies.As such, knowledge transfer, also known as transfer learning, between models is crucial for largescaledeployment of learned policies. Although transmission strategies have been the focus ofrecent concerns, they are mainly directed towards the fields of vision or language understandingand are not directly applicable to control environments where skill transfer is likely to happenbetween robots with different kinematic structures. The works presented in this thesis manuscriptfocus precisely on this point and aims at determining to what extent understanding between twomorphologically distinct entities is possible. This question is explored through the introductionof two distinct paradigms: Task-Centered and Teacher-Centered. The Task-Centered family oftechniques is based on the idea of the separation of task-related know-how from robot control policy.Such an independent kernel can therefore be passed on to other robots of different morphology andideally make it possible for the new agent to perform the task. In this context, several blueprintsfor creating this kernel are proposed and evaluated on a wide range of simulated environments.However, despite the attractive prospects of this formulation, the "one-size-fits-all" character ofTask-Centered techniques is not free of limitations which are extensively discussed. It is in thiscontext that Teacher-Centered approaches are introduced. Pursuing the same objective, theseinnovative procedure involve an expert agent from which the knowledge related to the task mustbe distilled into the target agent. To do this, an original metric is used to circumvent the structuraldifferences between the target agent and the expert agent and allow, despite this distinction, theerror to be back-propagated in order to optimize the agent.