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<p>Complex robotics domains (e.g., remote exploration applications and scenarios involving interactions with humans) require encoding high-level mission specifications that consider uncertainty. Most current fielded systems in practice require humans to manually encode mission specifications in ways that require amounts of time and expertise that can become infeasible and limit mission scope. Therefore, we propose a method of automating the process of encoding mission specifications as behavior trees. In particular, we present an algorithm for synthesizing behavior trees that represent the optimal policy for a user-defined specification of a domain and problem in the Probabilistic Planning Domain Definition Language (PPDDL). Our algorithm provides access to behavior tree advantages including compactness and modularity, while alleviating the need for the time-intensive manual design of behavior trees, which requires substantial expert knowledge. Our method converts the PPDDL specification into solvable MDP matrices, simplifies the solution, i.e. policy, using Boolean algebra simplification, and converts this simplified policy to a compact behavior tree that can be executed by a robot. We present simulated experiments for a marine target search and response scenario and an infant-robot interaction for mobility domain. Our results demonstrate that the synthesized, simplified behavior trees have approximately between 15&#xa0;<Emphasis FontCategory="SansSerif">x</Emphasis> and 26&#xa0;<Emphasis FontCategory="SansSerif">x</Emphasis> fewer nodes and an average of between 8&#xa0;<Emphasis FontCategory="SansSerif">x</Emphasis> and 13&#xa0;<Emphasis FontCategory="SansSerif">x</Emphasis> fewer active conditions for selecting the active action than they would without simplification. These compactness and activity results suggest an increase in the interpretability and execution efficiency of the behavior trees synthesized by the proposed method. Additionally, our results demonstrate that this synthesis method is robust to a variety of user input mistakes, and we empirically confirm that the synthesized behavior trees perform equivalently to the optimal policy that they are constructed to logically represent.</p>

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Synthesizing compact behavior trees for probabilistic robotics domains

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Emily Scheide,
  • Graeme Best,
  • Geoffrey A. Hollinger

摘要

Complex robotics domains (e.g., remote exploration applications and scenarios involving interactions with humans) require encoding high-level mission specifications that consider uncertainty. Most current fielded systems in practice require humans to manually encode mission specifications in ways that require amounts of time and expertise that can become infeasible and limit mission scope. Therefore, we propose a method of automating the process of encoding mission specifications as behavior trees. In particular, we present an algorithm for synthesizing behavior trees that represent the optimal policy for a user-defined specification of a domain and problem in the Probabilistic Planning Domain Definition Language (PPDDL). Our algorithm provides access to behavior tree advantages including compactness and modularity, while alleviating the need for the time-intensive manual design of behavior trees, which requires substantial expert knowledge. Our method converts the PPDDL specification into solvable MDP matrices, simplifies the solution, i.e. policy, using Boolean algebra simplification, and converts this simplified policy to a compact behavior tree that can be executed by a robot. We present simulated experiments for a marine target search and response scenario and an infant-robot interaction for mobility domain. Our results demonstrate that the synthesized, simplified behavior trees have approximately between 15 x and 26 x fewer nodes and an average of between 8 x and 13 x fewer active conditions for selecting the active action than they would without simplification. These compactness and activity results suggest an increase in the interpretability and execution efficiency of the behavior trees synthesized by the proposed method. Additionally, our results demonstrate that this synthesis method is robust to a variety of user input mistakes, and we empirically confirm that the synthesized behavior trees perform equivalently to the optimal policy that they are constructed to logically represent.