# DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation

arXiv: https://arxiv.org/abs/2608.00793 · HTML: https://mlsanity.com/paper/2608.00793/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 1 Aug 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 27 of 27)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://mlsanity.com/paper/2501.09747/) (2025)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations](https://mlsanity.com/paper/2412.14803/) (2024)
- [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025)
- [Any-point Trajectory Modeling for Policy Learning](https://mlsanity.com/paper/2401.00025/) (2023)
- [VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model](https://mlsanity.com/paper/2509.09372/) (2025)
- [Flow as the Cross-Domain Manipulation Interface](https://mlsanity.com/paper/2407.15208/) (2024)
- [InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy](https://mlsanity.com/paper/2510.13778/) (2025)
- [DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation](https://mlsanity.com/paper/2601.22153/) (2026)
- [Towards Generalizable Robotic Manipulation in Dynamic Environments](https://mlsanity.com/paper/2603.15620/) (2026)
- [InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization](https://mlsanity.com/paper/2607.04988/) (2026)
- [Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2606.08737/) (2026)
- [Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination](https://mlsanity.com/paper/2606.10040/) (2026)
- [Mask World Model: Predicting What Matters for Robust Robot Policy Learning](https://mlsanity.com/paper/2604.19683/) (2026)
- [FlowWAM: Optical Flow as a Unified Action Representation for World Action Models](https://mlsanity.com/paper/2607.13017/) (2026)
- [Pixel Motion as Universal Representation for Robot Control](https://mlsanity.com/paper/2505.07817/) (2025)
- [Token Expand-Merge: Training-Free Token Compression for Vision-Language-Action Models](https://mlsanity.com/paper/2512.09927/) (2025)
- [Robotic VLA Benefits from Joint Learning with Motion Image Diffusion](https://mlsanity.com/paper/2512.18007/) (2025)
- [Future Optical Flow Prediction Improves Robot Control & Video Generation](https://mlsanity.com/paper/2601.10781/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)

## Built on by (most cited first, 4 of 4)

- [DualManip: Agentic Dynamic Manipulation via Dual-Path Semantic Reasoning and Geometric Adaptation](https://mlsanity.com/paper/2609.31112/) (2026)
- [Beyond Visual Quality: A Study of Test-Time Planning with World Action Models](https://mlsanity.com/paper/2609.24745/) (2026)
- [Dynamic Manipulation with World-Action Models via Counterfactual Planning](https://mlsanity.com/paper/2609.33172/) (2026)
- [Event-Aligned Visual Action Reasoning for World Action Models](https://mlsanity.com/paper/2610.09427/) (2026)

## Abstract (arXiv)

Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on the current frame and execute large backbones synchronously, limiting motion awareness and responsive control in dynamic scenes. We propose DynamicWAM, a compact WAM for dynamic object manipulation with dual-path motion conditioning. DynamicWAM introduces history-flow conditioning, encoding temporally aligned optical-flow frames alongside the current observation through a frozen pretrained video VAE to preserve spatial motion structure, while injecting kinematic descriptors of displacement, duration, velocity, and acceleration into the action expert to provide motion magnitude and timing. The two complementary paths are fused through joint world-action attention. A distilled compact backbone and real-time chunking (RTC)-based asynchronous execution further enable responsive control. On DOMINO, DynamicWAM achieves a 38.2% success rate and a 53.2 manipulation score, outperforming all evaluated baselines. Across 12 real-world tasks spanning linear, circular, and compound target motion, it achieves a 46.7% average success rate, exceeding the strongest baseline by 22.9 percentage points.