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

• URL: http://arxiv.org/abs/2605.17815v1
• Date: Mon, 18 May 2026 03:41:59 GMT
• Title: Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement
• Abstract summary: This work focuses on augmenting the task planning domain with richer nonprehensile aggregating actions.<n>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.<n>Results highlight clear benefits of achieving faster execution than solely using pick-and-place actions.
• Score: 14.463441841507477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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