Bayesian Optimization for Sample-Efficient Policy Improvement in Robotic Manipulation
arxiv(2024)
摘要
Sample efficient learning of manipulation skills poses a major challenge in
robotics. While recent approaches demonstrate impressive advances in the type
of task that can be addressed and the sensing modalities that can be
incorporated, they still require large amounts of training data. Especially
with regard to learning actions on robots in the real world, this poses a major
problem due to the high costs associated with both demonstrations and
real-world robot interactions. To address this challenge, we introduce
BOpt-GMM, a hybrid approach that combines imitation learning with own
experience collection. We first learn a skill model as a dynamical system
encoded in a Gaussian Mixture Model from a few demonstrations. We then improve
this model with Bayesian optimization building on a small number of autonomous
skill executions in a sparse reward setting. We demonstrate the sample
efficiency of our approach on multiple complex manipulation skills in both
simulations and real-world experiments. Furthermore, we make the code and
pre-trained models publicly available at http://bopt-gmm. cs.uni-freiburg.de.
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