# Let's Push Things Forward: A Survey on Robot Pushing

arXiv: https://arxiv.org/abs/1905.05138 · HTML: https://mlsanity.com/paper/1905.05138/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 14 of 14)

- [O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning](https://mlsanity.com/paper/2106.15087/) (2021)
- [DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control](https://mlsanity.com/paper/2306.09010/) (2023)
- [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)
- [Automatically Prepare Training Data for YOLO Using Robotic In-Hand Observation and Synthesis](https://mlsanity.com/paper/2301.01441/) (2023)
- [Combining Planning and Diffusion for Mobility with Unknown Dynamics](https://mlsanity.com/paper/2410.06911/) (2024)
- [One-shot Learning for Autonomous Aerial Manipulation](https://mlsanity.com/paper/2206.01411/) (2022)
- [Collective Intelligence for 2D Push Manipulations with Mobile Robots](https://mlsanity.com/paper/2211.15136/) (2022)
- [Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning](https://mlsanity.com/paper/2506.15380/) (2025)
- [Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction](https://mlsanity.com/paper/2303.05393/) (2023)
- [Robot Grasping and Manipulation: A Prospective](https://mlsanity.com/paper/2303.07807/) (2023)
- [ArtReg: Visuo-Tactile based Pose Tracking and Manipulation of Unseen Articulated Objects](https://mlsanity.com/paper/2511.06378/) (2025)
- [Push Smarter, Not Harder: Hierarchical RL-Diffusion Policy for Efficient Nonprehensile Manipulation](https://mlsanity.com/paper/2512.10099/) (2025)
- [Visual Prompt Guided Unified Pushing Policy](https://mlsanity.com/paper/2602.19193/) (2026)

## Abstract (arXiv)

As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.