Learning Robot Motion in a Cluttered Environment Using Unreliable Human Skeleton Data Collected by a Single RGB Camera

Existing learning from demonstration (LfD) frame- works have difficulty dealing with unreliable and limited number of demonstrations. To address this issue, we proposed a new motion planning framework named experience-driven random tree con- nect with human demonstration (ERTC-HD), which can facilitate the identification of valid motions in cluttered environments by only using human skeleton information extracted from a single red, green, and blue (RGB) camera. The concept of this framework is to only extract the comprehensive features of human motion from unreliable demonstrations and use it as a rough clue for solving difficult planning problems instead of as a strict solution. During the process of ERTC-HD, robot motions generated from extracted comprehensive features of human motion are saved as a path experience and modified through the path adaptation process of the existing ERTC planner when transferring it to the new prob- lem. The results of three simulation experiments revealed that the ERTC-HD could identify valid motion in cluttered environments within a shorter time than other state-of-the-art planners, even when using unreliable demonstration data collected by a single RGB camera. Reducing the required accuracy of the original information resources can extend the range of LfD framework applications.