Imitation Learning with Inconsistent Demonstrations through Uncertainty-based Data Manipulation
Peter Valletta,Rodrigo Pérez-Dattari,Jens Kober,Peter Valletta,Rodrigo Pérez-Dattari,Jens Kober
Aleatoric uncertainty estimation, based on the observed training data, is applied for the detection of conflicts in a demonstration data set. The particular focus of this paper is the resolution of conflicting data resulting from scenarios with equivalent action choices, such as obstacle avoidance, path planning or multiple joint configurations. In terms of the estimated uncertainty, the proposed ...
