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Cited by 59AI-based Problem Solving and PlanningRobot Manipulation and LearningRobotic Path Planning Algorithms

Representing Robot Task Plans as Robust Logical-Dynamical Systems

Open access
Paxton, Chris, Ratliff, Nathan, Eppner, Clemens, Fox, Dieter·2019·2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros40897.2019.8967861
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Abstract

It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal condition. We propose a framework which we call Robust Logical-Dynamical Systems (RLDS), which combines the advantages of task representations like behavior trees with theoretical guarantees on performance. RLDS can also be constructed automatically from simple sequential task plans and will still achieve robust, reactive behavior in dynamic real-world environments. In this work, we describe both our proposed framework and a case study on a simple household manipulation task, with examples for how specific pieces can be implemented to achieve robust behavior. Finally, we show how in the context of these manipulation tasks, a combination of an RLDS with planning can achieve better results under adversarial conditions.

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Crossref · 10.1109/iros40897.2019.8967861

Cites

22 of 22 references verified0 citation errors · 0 not checkable

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  • RLDS gives a more concise problem description than behavior trees alone, but its online reactivity is limited to offline-planned scenarios.

    “RLDS results in a more concise problem description with respect to using BTs only, but online reactivity is again limited to the scenarios planned offline.”

    From Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics (2023) · cites this paper as [6]

    The Introduction and Related Work describe RLDS task descriptions as simpler than specifying a BT’s internal structure. The Execution section says plan construction occurs offline and policies are selected in near real time. But the paper does not say reactivity is limited to scenarios anticipated offline; the Introduction and Real-World Experiments describe adaptation to unforeseen interference. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Replanning for unforeseen contingencies consumes more resources than reacting.

    “For unforeseen contingencies, replanning would be necessary, which is more resource-demanding than reacting, as shown in the experimental results in [6].”

    From Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics (2023) · cites this paper as [6]

    The Simulation Experiments section says replanning is more computationally intensive than reactive execution, and Table I reports slightly longer completion times with replanning. But the Introduction and Results describe reactive recovery from unforeseen challenges, so the paper does not support the sentence’s assertion that such contingencies necessarily require replanning. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • To change the attraction region, one must either replan or expand the BT dynamically.

    “Alternatively, adapting the region of attraction requires either replanning [6] or dynamic BT expansion [4].”

    From Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics (2023) · cites this paper as [6]

    The Simulation Experiments section describes replanning when execution conditions fail and says this lets the robot adapt. The paper does not state that changing a region of attraction requires replanning; the dynamic BT expansion alternative is attributed to a different citation in the citing sentence. No page number is available in our copy. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 5

  1. [6]6. C. McGann, E. Berger, J. Bohren, S. Chitta, B. Gerkey, S. Glaser, B. Marthi, W. Meeussen, T. Pratkanis, E. Marder-Eppstein et al., "Model-based, hierarchical control of a mobile manipulation plat- form," in ICAPS Workshop on Planning and Plan Execution for Real- World Systems, Thessaloniki, Greece, 2009.
  2. [11]11. E. Gat, R. P. Bonnasso, R. Murphy et al., "On three-layer architec- tures," Artificial intelligence and mobile robots, vol. 195, p. 210, 1998.
  3. [14]14. J. L. Bresina, A. K. Jónsson, P. H. Morris, and K. Rajan, "Activity planning for the mars exploration rovers." in ICAPS, 2005, pp. 40-49.
  4. [17]17. M. Ghallab, A. Howe, C. Knoblock, D. McDermott, A. Ram, M. Veloso, D. Weld, and D. Wilkins, "Pddl-the planning domain definition language," AIPS-98 planning committee, vol. 3, p. 14, 1998.
  5. [21]21. M. Toussaint, "Logic-geometric programming: An optimization-based approach to combined task and motion planning," in Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.

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Related

Authors
Paxton, C., Ratliff, N., Eppner, C., & Fox, D.
Affiliations
Nvidia (United States) (US)
ORCID
Venue
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2019-11
Pages
5588-5595
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros40897.2019.8967861
OpenAlex
W2964906587
Cited by
59
References
22
Citations by year
2025: 4 · 2024: 15 · 2023: 2 · 2022: 13 · 2021: 12 · 2020: 10 · 2019: 3
FWCI
3.64
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
Keywords
robot task planning, behavior trees, robustness, recovery behavior

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