# MOVE: A Simple Motion-Based Data Collection Paradigm for Spatial Generalization in Robotic Manipulation

arXiv: https://arxiv.org/abs/2512.04813 · HTML: https://mlsanity.com/paper/2512.04813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 4 Dec 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 3 of 3)

- [HoloBrain-0 Technical Report](https://mlsanity.com/paper/2602.12062/) (2026)
- [Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning](https://mlsanity.com/paper/2606.20871/) (2026)
- [The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection](https://mlsanity.com/paper/2607.02322/) (2026)

## Abstract (arXiv)

Imitation learning method has shown immense promise for robotic manipulation, yet its practical deployment is fundamentally constrained by the data scarcity. Despite prior work on collecting large-scale datasets, there still remains a significant gap to robust spatial generalization. We identify a key limitation: individual trajectories, regardless of their length, are typically collected from a single, static spatial configuration of the environment. This includes fixed object and target spatial positions as well as unchanging camera viewpoints, which significantly restricts the diversity of spatial information available for learning. To address this critical bottleneck in data efficiency, we propose MOtion-Based Variability Enhancement (MOVE), a simple yet effective data collection paradigm that enables the acquisition of richer spatial information from dynamic demonstrations. Our core contribution is an augmentation strategy that injects motion into any movable objects within the environment for each demonstration. This process implicitly generates a dense and diverse set of spatial configurations within a single trajectory. We conduct extensive experiments in both simulation and real-world environments to validate our approach. For example, in simulation tasks requiring strong spatial generalization, MOVE achieves an average success rate of 39.1%, a 76.1% relative improvement over the static data collection paradigm (22.2%), and yields up to 2--5× gains in data efficiency on certain tasks. Our code is available at https://github.com/lucywang720/MOVE.