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Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization
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AI
DOI：10.1109/LRA.2023.3268598.png)
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The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.
Keyword:
Behavioral sciences
Decision trees
Planning
Batteries
Task analysis
Circuit synthesis
Partitioning algorithms
Behavior-based systems
intelligent transportation systems
learning from demonstration
期刊
I
IF:
5.3
论文数:
1.9K
被引数:
3.9W
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