Task Planning with Belief Behavior Trees
| Autor: | Lorenzo Natale, Evgenii Safronov, Michele Colledanchise |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Node (networking) 02 engineering and technology Extension (predicate logic) Task (project management) Set (abstract data type) Computer Science - Robotics 020901 industrial engineering & automation Action (philosophy) 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | IROS |
| Popis: | In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized following a planning strategy for BTs proposed recently: from a set of goal conditions we iteratively select a goal and find the action, or in general the subtree, that satisfies it. Such action may have preconditions that do not hold. For those preconditions, we find an action or subtree in the same fashion. We extend this approach by including, in the planner, actions that have the purpose to reduce the uncertainty that affects the value of a condition node in the BT (for example, turning on the lights to have better lighting conditions). We demonstrate that BBTs allows task planning with non-deterministic outcomes for actions. We provide experimental validation of our approach in a real robotic scenario and - for sake of reproducibility - in a simulated one. Acccepted to 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Databáze: | OpenAIRE |
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