# Diffusion-Based Imaginative Coordination for Bimanual Manipulation

arXiv: https://arxiv.org/abs/2507.11296 · HTML: https://mlsanity.com/paper/2507.11296/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2025.

## Built on by (most cited first, 7 of 7)

- [EVA: Aligning Video World Models with Executable Robot Actions via Inverse Dynamics Rewards](https://mlsanity.com/paper/2603.17808/) (2026)
- [Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation](https://mlsanity.com/paper/2602.23814/) (2026)
- [Hyper-GoalNet: Goal-Conditioned Manipulation Policy Learning with HyperNetworks](https://mlsanity.com/paper/2512.00085/) (2025)
- [See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model](https://mlsanity.com/paper/2605.11817/) (2026)
- [Continuous Vision-Language-Action Co-Learning with Semantic-Physical Alignment for Behavioral Cloning](https://mlsanity.com/paper/2511.14396/) (2025)
- [CUBic: Coordinated Unified Bimanual Perception and Control Framework](https://mlsanity.com/paper/2605.13452/) (2026)
- [JAMB: Joint Action-Motion Diffusion for Bimanual Manipulation](https://mlsanity.com/paper/2609.25322/) (2026)

## Abstract (arXiv)

Bimanual manipulation is crucial in robotics, enabling complex tasks in industrial automation and household services. However, it poses significant challenges due to the high-dimensional action space and intricate coordination requirements. While video prediction has been recently studied for representation learning and control, leveraging its ability to capture rich dynamic and behavioral information, its potential for enhancing bimanual coordination remains underexplored. To bridge this gap, we propose a unified diffusion-based framework for the joint optimization of video and action prediction. Specifically, we propose a multi-frame latent prediction strategy that encodes future states in a compressed latent space, preserving task-relevant features. Furthermore, we introduce a unidirectional attention mechanism where video prediction is conditioned on the action, while action prediction remains independent of video prediction. This design allows us to omit video prediction during inference, significantly enhancing efficiency. Experiments on two simulated benchmarks and a real-world setting demonstrate a significant improvement in the success rate over the strong baseline ACT using our method, achieving a 24.9% increase on ALOHA, an 11.1% increase on RoboTwin, and a 32.5% increase in real-world experiments. Our models and code are publicly available at https://github.com/return-sleep/DiffusionbasedimaginativeCoordination.