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| + | ====== Imitation Learning ====== | ||
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| + | To accomplish a particular manipulation task, a robot needs a detailed description | ||
| + | of how to execute it. | ||
| + | However, it is not possible to specify all potential tasks of a | ||
| + | manipulation robot beforehand. | ||
| + | operating in industrial contexts are frequently faced with changes in | ||
| + | the production process. As a consequence, | ||
| + | on a regular basis. | ||
| + | solutions that enable normal users to quickly and intuitively teach | ||
| + | new manipulation skills to a robot. | ||
| + | |||
| + | |||
| + | More concretely, we consider the problem of learning generalized descriptions of | ||
| + | object manipulation tasks from human demonstrations. | ||
| + | We employ dynamic Bayesian | ||
| + | networks (DBN) as a compact representation where special nodes | ||
| + | encode geometrical constraints between the relevant objects in the scene and the | ||
| + | hand of the demonstrator. This formulation allows the robot to | ||
| + | learn generalized task descriptions from multiple demonstrations so that | ||
| + | it can reproduce them also under changed conditions. | ||
| + | Furthermore, | ||
| + | can easily be added during the reproduction of a task. This is, for example, relevant to allow a robot | ||
| + | to deal with obstacles. | ||
| + | the robot searches for the action sequence that maximizes | ||
| + | the data likelihood in the DBN. In experiments carried out in simulation as well as on | ||
| + | a real manipulation robot, we show that | ||
| + | our approach enables robots to efficiently learn novel manipulation skills from human | ||
| + | demonstrations and to robustly reproduce them in different situations. | ||
| + | |||
| + | < | ||
| + | <iframe width=" | ||
| + | </ | ||
| + | |||
| + | ====== Related Publications ====== | ||
| + | |||
| + | < | ||
| + | < | ||
| + | < | ||
| + | </ | ||
