RE:BT-Espresso: Improving Interpretability and Expressivity of Behavior Trees Learned from Robot Demonstrations

Wathieu, Adam; Groechel, Thomas R.; Lee, Haemin Jenny; Kuo, C.-C. Jay; Matarić, Maja J.

doi:10.1109/icra46639.2022.9812046

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…There has been some previous work on combining LfD and BTs [14], [16], [28]- [31]. The method proposed in [28], [29], and [32] learns a Decision Tree (DT) to map from state space to action space. The DT is then converted into a BT using the fact that BTs generalize DTs [4].…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Learning Behavior Trees in Collaborative Robotic Applications

Iovino¹,

Styrud²,

Falco³

et al. 2023

Preprint

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In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behavior Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.

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Section: Related Workmentioning

confidence: 99%

“…The BT required a final tuning of its parameters, thus limiting its usage. This was extended by [29] and improved in [32] who generalized it to use any logic minimizer on the DT. The solution is implemented on a mobile manipulator to perform a house-cleaning task.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Learning Behavior Trees in Collaborative Robotic Applications

Iovino¹,

Styrud²,

Falco³

et al. 2023

Preprint

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“…Proposed approaches to full mission aerial autonomy typically involve a high-level decision maker encoded as a finite state machine (Gómez-Anaya et al 2014; Kalaitzakis et al 2021; Krátký et al 2021; Rodriguez-Ramos et al 2022; Petrlik et al 2023) or behavior tree (Molina et al 2020; Safronov et al 2019). We are particularly interested in behavior trees (Colledanchise and Ögren 2018), which are common in computer games and have recently gained popularity in robotics due to the ease of design and online introspection (Iovino et al 2022, 2023; Safronov et al 2020; Scheide et al 2021; Sprague et al 2018; Wathieu et al 2022). We demonstrate the use of behavior trees by physical aerial robot systems in missions involving many complex behaviors that aid resiliency.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

Best,

Garg,

Keller

et al. 2023

The International Journal of Robotics Research

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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.

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