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

arXiv: https://arxiv.org/abs/2605.17815 · HTML: https://mlsanity.com/paper/2605.17815/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 14 of 14)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances](https://mlsanity.com/paper/2108.04145/) (2021)
- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [Anytime Integrated Task and Motion Policies for Stochastic Environments](https://mlsanity.com/paper/1904.13006/) (2019)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Persistent Homology for Effective Non-Prehensile Manipulation](https://mlsanity.com/paper/2202.02937/) (2022)
- [Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints](https://mlsanity.com/paper/2005.09127/) (2020)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting](https://mlsanity.com/paper/2010.10064/) (2020)
- [tt MORALS: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space](https://mlsanity.com/paper/2310.03246/) (2023)
- [Efficient, High-Quality Stack Rearrangement](https://mlsanity.com/paper/1706.09949/) (2017)

## Abstract (arXiv)

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.