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arxiv: 2605.17815 · v1 · pith:WE6YU6Z5new · submitted 2026-05-18 · 💻 cs.RO · cs.AI

Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement

Hao Lu , Rahul Shome This is my paper

Pith reviewed 2026-05-20 11:05 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords stack rearrangementtopple actionstask planningpebble motion problemnonprehensile manipulationtabletop settingsaggregating gadgetIsaacSim simulation
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The pith

Interleaving topple actions with pick-and-place reduces execution time in tabletop stack rearrangement.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that incorporating topple actions into planning for rearranging stacks on a table can shorten overall execution by collapsing sequences of necessary relocations. It models the problem using a directed graphical abstraction where objects are treated as pebbles and introduces a novel aggregating gadget specifically for topple actions. This allows generating candidate task plans that mix toppling and picking as needed for each problem instance. The approach is validated in physics simulation, showing faster performance than plans restricted to pick-and-place alone. Similar modeling can extend to other aggregating actions such as scooping.

Core claim

Using a novel aggregating gadget for topple within a directed graphical abstraction, task planning for stack rearrangement reduces to a variant of the pebble motion problem, permitting plans that interleave pick-and-place and topple actions to achieve faster execution than pick-and-place only.

What carries the argument

The novel aggregating gadget for topple, which enables modeling the action in the graphical abstraction so that toppling can compress long sequences of intermediate object relocations in the pebble-motion planning.

If this is right

  • Computed plans interleave topple and pick-and-place actions based on the specific rearrangement problem.
  • Execution in IsaacSim simulation is faster than using solely pick-and-place actions.
  • Similar abstractions can model other aggregating actions such as scoop.
  • The method provides benefits for efficient object manipulation in automation applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This abstraction approach could be tested with physical robots to check transferability beyond simulation.
  • Extending the gadget to more complex stacking scenarios might reveal limits of the pebble-motion modeling.
  • Other nonprehensile actions in robotics could benefit from analogous directed graph gadgets for planning.
  • Integrating this into broader task planners might improve efficiency in cluttered tabletop environments.

Load-bearing premise

The directed graphical abstraction and aggregating gadget for topple produce plans that remain physically valid and executable when transferred from the pebble-motion model to actual robot actions in the IsaacSim environment.

What would settle it

Running the generated plans in the IsaacSim environment and observing whether all topple actions execute without unintended object movements or instabilities that the model did not predict.

Figures

Figures reproduced from arXiv: 2605.17815 by Hao Lu, Rahul Shome.

Figure 1
Figure 1. Figure 1: Certain stack rearrangement tasks can be made [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The SMASH gadget over which the object rearrangement problem is modeled as a modified pebble motion problem [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pick and place gadget with 3 stacks. Prior work in object stack rearrange￾ment [2] introduced gadget construc￾tions in which a pick–and–place stack rearrangement instance can be repre￾sented as moving ℓ object “pebbles” on a directed graph. We use a variant with equivalent connectivity properties ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Task planning ablation for 9-block instances from the multi-goal rearrangement benchmark. Left: success rate (%) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: The figure shows the start and goal arrangements [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: A scoop action (above) can use similar gadgets (below). This failure introduces an in￾teresting trade-off for the ef￾ficiency benefits obtained from toppling – toppling remains a dynamic, uncertain action whose exact grounding is resolved only at execution time. This motivates future investigation into charac￾terizing robust execution strate￾gies [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Efficient object manipulation strategies have significant impact in automation applications. In this work, the stack rearrangement in tabletop settings is studied, with a focus on augmenting the task planning domain with richer nonprehensile aggregating actions, in particular the toppling of objects from a stack to the table. Toppling can compress long sequences of intermediate relocations. Computed plans need to interleave pick-and-place actions with topple throughout its plan based on the problem. In order to generate the task plan and model an abstraction to compute solutions that include both pick-and-place and topple actions, a novel aggregating gadget for topple is introduced. Using this directed graphical abstraction, candidate task plan computation becomes a variant of the pebble motion problem, treating objects as pebbles. Benchmarks are then reported in a IsaacSim-based physics simulation. Results highlight clear benefits of achieving faster execution than solely using pick-and-place actions. Though this work primarily investigates the topple action, we demonstrate that similar abstractions can model other aggregating actions of interest, like scoop. The current work provides a preliminary, strong indication of the promising benefits of abstractions for rich object interactions in manipulation applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper studies stack rearrangement on tabletops by augmenting task planning with nonprehensile topple actions in addition to pick-and-place. It introduces a novel aggregating gadget inside a directed graphical abstraction that reduces candidate plan generation to a pebble-motion problem on objects treated as pebbles. Plans interleaving topple and pick-and-place are computed and transferred to an IsaacSim physics simulator; the authors report faster execution times than pick-and-place-only baselines and sketch extensions to other aggregating actions such as scooping.

Significance. If the generated plans remain kinematically and dynamically valid when instantiated with real robot trajectories and object geometries, the work supplies a concrete, reusable abstraction for incorporating nonprehensile primitives into combinatorial manipulation planning. The pebble-motion reduction and the explicit gadget construction are technically clean contributions that could generalize beyond toppling.

major comments (2)
  1. [§3] §3 (Aggregating gadget and graphical abstraction): the gadget encodes topple as a discrete state transition on an implicit graph, yet the manuscript does not specify how stack height, contact normals, friction, or object geometry are (or are not) folded into the transition rules. Because these parameters are absent from the pebble-motion model, it is possible for the planner to emit topple sequences that succeed in the abstract graph but require unmodeled recovery motions or fail outright in IsaacSim, directly undermining the reported execution-time gains.
  2. [Benchmarks / Results] Benchmarks / Results section: the claim of “clear benefits” and “faster execution” is central, yet the manuscript supplies no tabulated execution times, success rates, number of trials, or statistical comparison against the pick-and-place baseline. Without these data the quantitative advantage cannot be verified and the transfer from abstract plan to simulator remains unproven.
minor comments (2)
  1. Notation for the pebble-motion variant should be defined more explicitly (e.g., state space, move set, and cost function) so that the reduction from the gadget-augmented graph is reproducible.
  2. Add citations to prior pebble-motion literature in robotics and to existing nonprehensile manipulation planners to situate the novelty of the aggregating gadget.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Aggregating gadget and graphical abstraction): the gadget encodes topple as a discrete state transition on an implicit graph, yet the manuscript does not specify how stack height, contact normals, friction, or object geometry are (or are not) folded into the transition rules. Because these parameters are absent from the pebble-motion model, it is possible for the planner to emit topple sequences that succeed in the abstract graph but require unmodeled recovery motions or fail outright in IsaacSim, directly undermining the reported execution-time gains.

    Authors: We appreciate the referee pointing out the need for greater clarity on the physical assumptions underlying the aggregating gadget. The gadget is formulated as a discrete combinatorial abstraction that reduces planning to a pebble-motion problem; the transition rules implicitly assume rigid bodies, a flat table surface, and friction sufficient to enable toppling without unintended sliding. Physical feasibility is verified after plan generation by executing the sequence in the IsaacSim simulator. We agree that an explicit statement of these modeling assumptions and a discussion of potential mismatches with simulation would improve the section. In the revised manuscript we will expand §3 with a dedicated paragraph on assumptions regarding stack height, contact geometry, and friction, together with a brief note on how the simulator serves as the final validity check. revision: yes

  2. Referee: [Benchmarks / Results] Benchmarks / Results section: the claim of “clear benefits” and “faster execution” is central, yet the manuscript supplies no tabulated execution times, success rates, number of trials, or statistical comparison against the pick-and-place baseline. Without these data the quantitative advantage cannot be verified and the transfer from abstract plan to simulator remains unproven.

    Authors: We concur that the quantitative claims would be stronger if supported by tabulated data and statistical analysis. Although the manuscript states that plans were transferred to IsaacSim and produced faster execution than the pick-and-place baseline, we did not include explicit tables, trial counts, success rates, or statistical comparisons. This was an oversight in presentation. In the revised version we will augment the Benchmarks / Results section with a table reporting mean execution times, standard deviations, success rates over repeated trials, and direct numerical comparison to the baseline, thereby making the reported gains verifiable and the simulator transfer explicit. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces a novel aggregating gadget as an independent modeling abstraction that reduces topple-inclusive planning to a pebble-motion variant on a directed graph. Candidate plans are generated from this model and then transferred to IsaacSim for empirical benchmarking of execution time. No equations, parameters, or results are shown to reduce to their own inputs by construction; there are no fitted inputs renamed as predictions, no load-bearing self-citations, and no self-definitional loops. The reported speed benefits are external simulation outcomes, rendering the chain self-contained against physical benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Ledger entries are inferred from the abstract description only, as the full manuscript was not available for detailed inspection.

axioms (1)
  • domain assumption Toppling can be faithfully represented by a directed graphical aggregating gadget that aggregates multiple objects in one action.
    This modeling choice is introduced as the core abstraction enabling the pebble-motion reduction.
invented entities (1)
  • Aggregating gadget for topple no independent evidence
    purpose: To model toppling as an atomic action within a directed graph for task planning.
    Presented as a novel construct that allows interleaving topple with pick-and-place.

pith-pipeline@v0.9.0 · 5729 in / 1225 out tokens · 45809 ms · 2026-05-20T11:05:22.947147+00:00 · methodology

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 2 internal anchors

  1. [1]

    Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

    S. D. Han, N. M. Stiffler, A. Krontiris, K. Bekris, and J. Yu, “Com- plexity results and fast methods for optimal tabletop rearrangement with overhand grasps,”arXiv:1711.07369, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  2. [2]

    Efficient, high- quality stack rearrangement,

    S. D. Han, N. M. Stiffler, K. E. Bekris, and J. Yu, “Efficient, high- quality stack rearrangement,”IEEE RAL, vol. 3, no. 3, pp. 1608–1615, 2018

    work page 2018

  3. [3]

    A framework for push-grasping in clutter,

    M. Dogar and S. Srinivasa, “A framework for push-grasping in clutter,” Robotics: Science and systems VII, vol. 1, pp. 65–72, 2011

    work page 2011

  4. [4]

    Large-scale multi-object rear- rangement,

    E. Huang, Z. Jia, and M. T. Mason, “Large-scale multi-object rear- rangement,” in2019 international conference on robotics and automa- tion (ICRA). IEEE, 2019, pp. 211–218

    work page 2019

  5. [5]

    Persistent homology for effective non-prehensile manipula- tion,

    E. R. Vieira, D. Nakhimovich, K. Gao, R. Wang, J. Yu, and K. E. Bekris, “Persistent homology for effective non-prehensile manipula- tion,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 1918–1924

    work page 2022

  6. [6]

    Toppling manipulation,

    K. M. Lynch, “Toppling manipulation,” inProceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol. 4. IEEE, 1999, pp. 2551–2557

    work page 1999

  7. [7]

    An incremental constraint-based framework for task and motion planning,

    N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “An incremental constraint-based framework for task and motion planning,” The International Journal of Robotics Research, vol. 37, no. 10, pp. 1134–1151, 2018

    work page 2018

  8. [8]

    Task and motion planning for execution in the real,

    T. Pan, R. Shome, and L. E. Kavraki, “Task and motion planning for execution in the real,”IEEE Transactions on Robotics, vol. 40, pp. 3356–3371, 2024

    work page 2024

  9. [9]

    Rearrangement of multiple movable objects-integration of global and local planning methodology,

    J. Ota, “Rearrangement of multiple movable objects-integration of global and local planning methodology,” inICRA, vol. 2, 2004

    work page 2004

  10. [10]

    Manipula- tion planning among movable obstacles,

    M. Stilman, J.-U. Schamburek, J. Kuffner, and T. Asfour, “Manipula- tion planning among movable obstacles,” inICRA, 2007

    work page 2007

  11. [11]

    Synchronized multi-arm rearrangement guided by mode graphs with capacity constraints,

    R. Shome and K. E. Bekris, “Synchronized multi-arm rearrangement guided by mode graphs with capacity constraints,” inInternational Workshop on the Algorithmic Foundations of Robotics. Springer, 2020, pp. 243–260

    work page 2020

  12. [12]

    Towards robust product packing with a minimalistic end-effector,

    R. Shome, W. N. Tang, C. Song, C. Mitash, H. Kourtev, J. Yu, A. Boularias, and K. E. Bekris, “Towards robust product packing with a minimalistic end-effector,” inIEEE International Conference on Robotics and Automation (ICRA), 2019

    work page 2019

  13. [13]

    Decision making in joint push-grasp action space for large-scale object sorting,

    Z. Pan and K. Hauser, “Decision making in joint push-grasp action space for large-scale object sorting,” in2021 IEEE international conference on robotics and automation (ICRA). IEEE, 2021, pp. 6199–6205

    work page 2021

  14. [14]

    Integrated task and motion planning,

    C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kael- bling, and T. Lozano-P ´erez, “Integrated task and motion planning,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, no. 1, pp. 265–293, 2021

    work page 2021

  15. [15]

    Combined heuristic task and motion planning for bi-manual robots,

    A. Akbari, F. Lagriffoul, and J. Rosell, “Combined heuristic task and motion planning for bi-manual robots,”Autonomous Robots, pp. 1–16, 2018

    work page 2018

  16. [16]

    Ffrob: Leverag- ing symbolic planning for efficient task and motion planning,

    C. R. Garrett, T. Lozano-Perez, and L. P. Kaelbling, “Ffrob: Leverag- ing symbolic planning for efficient task and motion planning,”IJRR, vol. 37, no. 1, pp. 104–136, 2018

    work page 2018

  17. [17]

    Hierarchical Task and Motion Planning in the Now,

    L. P. Kaelbling and T. Lozano-P ´erez, “Hierarchical Task and Motion Planning in the Now,” inICRA, 2011

    work page 2011

  18. [18]

    Anytime integrated task and motion policies for stochastic environments,

    N. Shah, D. K. Vasudevan, K. Kumar, P. Kamojjhala, and S. Sri- vastava, “Anytime integrated task and motion policies for stochastic environments,” in2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 9285–9291

    work page 2020

  19. [19]

    Failure is an option: task and motion planning with failing executions,

    T. Pan, A. M. Wells, R. Shome, and L. E. Kavraki, “Failure is an option: task and motion planning with failing executions,” in2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 1947–1953

    work page 2022

  20. [20]

    Long-horizon manipulation of unknown objects via task and motion planning with estimated Affordances,

    A. Curtis, X. Fang, L. P. Kaelbling, T. Lozano-P ´erez, and C. R. Garrett, “Long-horizon manipulation of unknown objects via task and motion planning with estimated Affordances,” in2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1940– 1946

    work page 2022

  21. [21]

    Diffusion policy: Visuomotor policy learning via ac- tion diffusion,

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y . Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via ac- tion diffusion,”The International Journal of Robotics Research, p. 02783649241273668, 2023

    work page 2023

  22. [22]

    Morals: Analysis of high-dimensional robot controllers via topological tools in a latent space,

    E. R. Vieira, A. Sivaramakrishnan, S. Tangirala, E. Granados, K. Mis- chaikow, and K. E. Bekris, “Morals: Analysis of high-dimensional robot controllers via topological tools in a latent space,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 27–33

    work page 2024

  23. [23]

    Gurobi Optimizer Reference Manual,

    Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023

    work page 2023

  24. [24]

    Robot operating system 2: Design, architecture, and uses in the wild,

    S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot operating system 2: Design, architecture, and uses in the wild,” Science Robotics, vol. 7, no. 66, p. eabm6074, 2022

    work page 2022

  25. [25]

    Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

    D. Coleman, I. Sucan, S. Chitta, and N. Correll, “Reducing the barrier to entry of complex robotic software: a MoveIT! case study,”arXiv preprint arXiv:1404.3785, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  26. [26]

    The open motion planning library,

    I. A. Sucan, M. Moll, and L. E. Kavraki, “The open motion planning library,”IEEE Robotics & Automation Magazine, vol. 19, no. 4, pp. 72–82, 2012

    work page 2012

  27. [27]

    Rrt-connect: An efficient approach to single-query path planning,

    J. Kuffner and S. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” inProceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automa- tion. Symposia Proceedings (Cat. No.00CH37065), vol. 2, 2000, pp. 995–1001 vol.2

    work page 2000