# More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing

arXiv: https://arxiv.org/abs/1604.04038 · HTML: https://mlsanity.com/paper/1604.04038/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2016.

## Built on by (most cited first, 30 of 31)

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- [AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents](https://mlsanity.com/paper/2401.12963/) (2024)
- [End-to-End Affordance Learning for Robotic Manipulation](https://mlsanity.com/paper/2209.12941/) (2022)
- [QUAR-VLA: Vision-Language-Action Model for Quadruped Robots](https://mlsanity.com/paper/2312.14457/) (2023)
- [AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation](https://mlsanity.com/paper/2407.07889/) (2024)
- [Learning rigid dynamics with face interaction graph networks](https://mlsanity.com/paper/2212.03574/) (2022)
- [Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions](https://mlsanity.com/paper/2511.04665/) (2025)
- [HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation](https://mlsanity.com/paper/2305.03942/) (2023)
- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation](https://mlsanity.com/paper/2503.16806/) (2025)
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- [HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks](https://mlsanity.com/paper/2103.09439/) (2021)
- [Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations](https://mlsanity.com/paper/2203.02107/) (2022)
- [Control-oriented Clustering of Visual Latent Representation](https://mlsanity.com/paper/2410.05063/) (2024)
- [MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation](https://mlsanity.com/paper/2510.18316/) (2025)
- [Object and Relation Centric Representations for Push Effect Prediction](https://mlsanity.com/paper/2102.02100/) (2021)
- [Learning Sequential Latent Variable Models from Multimodal Time Series Data](https://mlsanity.com/paper/2204.10419/) (2022)
- [Latent Adaptive Planner for Dynamic Manipulation](https://mlsanity.com/paper/2505.03077/) (2025)
- [Integrated Object Deformation and Contact Patch Estimation from Visuo-Tactile Feedback](https://mlsanity.com/paper/2305.14470/) (2023)
- [Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation](https://mlsanity.com/paper/2411.14913/) (2024)
- [ViTacGen: Robotic Pushing with Vision-to-Touch Generation](https://mlsanity.com/paper/2510.14117/) (2025)
- [An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning](https://mlsanity.com/paper/2108.01034/) (2021)
- [Localized Graph-Based Neural Dynamics Models for Terrain Manipulation](https://mlsanity.com/paper/2503.23270/) (2025)
- [Single RGB-D Camera Teleoperation for General Robotic Manipulation](https://mlsanity.com/paper/2106.14396/) (2021)

## Abstract (arXiv)

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move. In reality, however, robots often rely on approximations which yield models that are computable, but also restricted and inaccurate. Just how close are those models? How reasonable are the assumptions they are based on? To help answer these questions, and to get a better experimental understanding of pushing, we present a comprehensive and high-fidelity dataset of planar pushing experiments. The dataset contains timestamped poses of a circular pusher and a pushed object, as well as forces at the interaction.We vary the push interaction in 6 dimensions: surface material, shape of the pushed object, contact position, pushing direction, pushing speed, and pushing acceleration. An industrial robot automates the data capturing along precisely controlled position-velocity-acceleration trajectories of the pusher, which give dense samples of positions and forces of uniform quality. We finish the paper by characterizing the variability of friction, and evaluating the most common assumptions and simplifications made by models of frictional pushing in robotics.