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Learning from Demonstration (LfD) is a popular approach that allows humans toteach robots new skills by showing the correct way(s) of performing the desiredskill. Human-provided demonstrations, however, are not always optimal and theteacher usually addresses this issue by discarding or replacing sub-optimal(noisy or faulty) demonstrations. We propose a novel LfD representation thatlearns from both successful and failed demonstrations of a skill. Our approachencodes the two subsets of captured demonstrations (labeled by the teacher)into a statistical skill model, constructs a set of quadratic costs, and findsan optimal reproduction of the skill under novel problem conditions (i.e.constraints). The optimal reproduction balances convergence towards successfulexamples and divergence from failed examples. We evaluate our approach throughseveral 2D and 3D experiments in real-world using a UR5e manipulator arm andalso show that it can reproduce a skill from only failed demonstrations. Thebenefits of exploiting both failed and successful demonstrations are shownthrough comparison with two existing LfD approaches. We also compare ourapproach against an existing skill refinement method and show its capabilitiesin a multi-coordinate setting.

Learning from Successful and Failed Demonstrations via Optimization