Formalizing the Execution Context of Behavior Trees for Runtime Verification of Deliberative Policies

The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized for execution speed. We apply it to the use-case of a robot moving in an unstructured environment as its battery discharges, both in simulation and in the real world. The results of our experiments confirm that our approach can learn temporal formulas explaining task failures in a case of practical interest.

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“…The YARP simulation 10 used in [32] takes place in the IIT open space and "kitchen" area (see fig. 1a).…”

Section: A Yarp Simulationmentioning

confidence: 99%

Learning Linear Temporal Properties for Autonomous Robotic Systems

Ghiorzi

Colledanchise

Piquet

et al. 2023

IEEE Robot. Autom. Lett.

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“…Conditional Behavior Trees (CBTs) (Giunchiglia et al 2019) attach preconditions and effects (as used in classical planning) to execution nodes in behavior trees. This makes it possible to verify that conditions are guaranteed to be satisfied when reaching an execution node, e.g., by converting a conditional BT to a Boolean satisfiability problem (Giunchiglia et al 2019;Colledanchise et al 2021). This approach does not perform pruning on BTs beforehand and selector (fallback) control flow nodes require a quadratic number of symbols.…”

Section: Related Workmentioning

confidence: 99%

Planning for Automated Testing of Implicit Constraints in Behavior Trees

Köckemann

Calisi²,

Gemignani³

et al. 2023

ICAPS

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Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e.g., because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e.g., supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes.

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