Print Email Facebook Twitter Knowledge- and ambiguity-aware robot learning from corrective and evaluative feedback Title Knowledge- and ambiguity-aware robot learning from corrective and evaluative feedback Author Celemin, Carlos (TU Delft Learning & Autonomous Control) Kober, J. (TU Delft Learning & Autonomous Control) Date 2023 Abstract In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL method that improves the human–robot interaction for non-expert and imperfect teachers in two directions. First, uncertainty estimation is included to endow the agents with a lack of knowledge awareness (epistemic uncertainty) and demonstration ambiguity awareness (aleatoric uncertainty), such that the robot can request human input when it is deemed more necessary. Second, the proposed method enables the teachers to train with the flexibility of using corrective demonstrations, evaluative reinforcements, and implicit positive feedback. The experimental results show an improvement in learning convergence with respect to other learning methods when the agent learns from highly ambiguous teachers. Additionally, in a user study, it was found that the components of the proposed method improve the teaching experience and the data efficiency of the learning process. Subject Active learningCorrective demonstrationsHuman reinforcementInteractive imitation learningUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:010c1cec-3809-4a4b-b971-bc499462763d DOI https://doi.org/10.1007/s00521-022-08118-z ISSN 0941-0643 Source Neural Computing and Applications Part of collection Institutional Repository Document type journal article Rights © 2023 Carlos Celemin, J. Kober Files PDF s00521_022_08118_z.pdf 1.42 MB Close viewer /islandora/object/uuid:010c1cec-3809-4a4b-b971-bc499462763d/datastream/OBJ/view