2020
Learning Motion Parameterizations of Mobile Pick and Place Actions from Observing Humans in Virtual Environments
IROS 2020poster
In this paper, we present an approach and an implemented pipeline for transferring data acquired from observing humans in virtual environments onto robots acting in the real world, and adapting the data accordingly to achieve successful task execution. We demonstrate our pipeline by inferring seven…