# Learning to Singulate Objects using a Push Proposal Network

arXiv: https://arxiv.org/abs/1707.08101 · HTML: https://mlsanity.com/paper/1707.08101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

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

- [Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach](https://mlsanity.com/paper/2207.09314/) (2022)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [Precision-Focused Reinforcement Learning Model for Robotic Object Pushing](https://mlsanity.com/paper/2411.08622/) (2024)
- [SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences](https://mlsanity.com/paper/2107.02069/) (2021)
- [Visual Prompt Guided Unified Pushing Policy](https://mlsanity.com/paper/2602.19193/) (2026)

## Abstract (arXiv)

Learning to act in unstructured environments, such as cluttered piles of objects, poses a substantial challenge for manipulation robots. We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions. Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes. The model is designed to propose meaningful push actions based on over-segmented RGB-D images. We evaluate our approach by singulating up to 8 unknown objects in clutter. We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions. Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes, as well as to arbitrary object configurations. Videos of our experiments can be viewed at http://robotpush.cs.uni-freiburg.de