Bayesian Active Learning for Sim-to-Real Robotic Perception
Jianxiang Feng,Jongseok Lee,Maximilian Durner,Rudolph Triebel,Jianxiang Feng,Jongseok Lee,Maximilian Durner,Rudolph Triebel
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an efficient acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we employ deep Ba...
