This paper presents how the hybrid combination of behavior trees and the neuroscientific principle of active inference can be used for action planning and execution for reactive robot behaviors in dynamic environments. We show how complex robotic tasks can be formulated as a free-energy minimisation problem, and how state estimation and symbolic decision making are handled within the same framework. The general behavior is specified offline through behavior trees, where the leaf nodes represent desired states, not actions as in classical behavior trees. The decision of which action to execute to reach a state is left to the online active inference routine, in order to resolve unexpected contingencies. This hybrid combination improves the robustness of plans specified through behavior trees, while allowing to cope with the curse of dimensionality in active inference. The properties of the proposed algorithm are analysed in terms of robustness and convergence, and the theoretical results are validated using a mobile manipulator in a retail environment.
翻译:本文介绍了行为树和活跃推断神经科学原理的混合组合如何用于动态环境中反应性机器人行为的行动规划和执行。 我们展示了如何将复杂的机器人任务设计成一个自由能源最小化问题,以及如何在同一框架内处理国家估计和象征性决策。 一般行为通过行为树从网络外指定,在行为树上,叶节点代表了理想状态,而不是像古典行为树中那样的行动。为了解决意外意外的意外情况,将采取行动达到状态的决定留给网上主动推断常规。这种混合组合改善了行为树上规定的计划的稳健性,同时能够应对主动推论中维度的诅咒。从稳健性和趋同的角度分析了拟议算法的特性,并在零售环境中使用移动操纵器验证了理论结果。