# Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting

arXiv: https://arxiv.org/abs/2010.10064 · HTML: https://mlsanity.com/paper/2010.10064/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Learning Physically Realizable Skills for Online Packing of General 3D Shapes](https://mlsanity.com/paper/2212.02094/) (2022)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task](https://mlsanity.com/paper/2404.08424/) (2024)
- [Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement](https://mlsanity.com/paper/2605.17815/) (2026)

## Abstract (arXiv)

We present a planner for large-scale (un)labeled object sorting tasks, which uses two types of manipulation actions: overhead grasping and planar pushing. The grasping action offers completeness guarantee under mild assumptions, and planar pushing is an acceleration strategy that moves multiple objects at once. Our main contribution is twofold: (1) We propose a bilevel planning algorithm. Our high-level planner makes efficient, near-optimal choices between pushing and grasping actions based on a cost model. Our low-level planner computes one-step greedy pushing or grasping actions. (2) We propose a novel low-level push planner that can find one-step greedy pushing actions in a semi-discrete search space. The structure of the search space allows us to efficient We show that, for sorting up to 200 objects, our planner can find near-optimal actions with 10 seconds of computation on a desktop machine.