# DADP: Domain Adaptive Diffusion Policy

arXiv: https://arxiv.org/abs/2602.04037 · HTML: https://mlsanity.com/paper/2602.04037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 38 of its 43 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 30 of 38)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [In-context Reinforcement Learning with Algorithm Distillation](https://mlsanity.com/paper/2210.14215/) (2022)
- [Prompting Decision Transformer for Few-Shot Policy Generalization](https://mlsanity.com/paper/2206.13499/) (2022)
- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2005.06800/) (2020)
- [MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL](https://mlsanity.com/paper/2305.19923/) (2023)
- [Adapting Rapid Motor Adaptation for Bipedal Robots](https://mlsanity.com/paper/2205.15299/) (2022)
- [What Makes a Good Diffusion Planner for Decision Making?](https://mlsanity.com/paper/2503.00535/) (2025)
- [Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning](https://mlsanity.com/paper/2206.10442/) (2022)
- [HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning](https://mlsanity.com/paper/2508.21043/) (2025)
- [Context is Everything: Implicit Identification for Dynamics Adaptation](https://mlsanity.com/paper/2203.05549/) (2022)
- [ODRL: A Benchmark for Off-Dynamics Reinforcement Learning](https://mlsanity.com/paper/2410.20750/) (2024)
- [Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2403.19925/) (2024)
- [DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation](https://mlsanity.com/paper/2503.16806/) (2025)
- [LocoFormer: Generalist Locomotion via Long-context Adaptation](https://mlsanity.com/paper/2509.23745/) (2025)
- [Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning](https://mlsanity.com/paper/2405.06192/) (2024)
- [Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling](https://mlsanity.com/paper/2406.00079/) (2024)
- [Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement](https://mlsanity.com/paper/2410.11448/) (2024)
- [Skill Expansion and Composition in Parameter Space](https://mlsanity.com/paper/2502.05932/) (2025)
- [Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning](https://mlsanity.com/paper/2305.00286/) (2023)

## Built on by (most cited first, 1 of 1)

- [DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors](https://mlsanity.com/paper/2604.25050/) (2026)

## Abstract (arXiv)

Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control. Substantial progress has been made through domain representation learning to capture domain-specific information, thus enabling domain-aware decision making. We analyze the process of learning domain representations through dynamical prediction and find that selecting contexts adjacent to the current step causes the learned representations to entangle static domain information with varying dynamical properties. Such mixture can confuse the conditioned policy, thereby constraining zero-shot adaptation. To tackle the challenge, we propose DADP (Domain Adaptive Diffusion Policy), which achieves robust adaptation through unsupervised disentanglement and domain-aware diffusion injection. First, we introduce Lagged Context Dynamical Prediction, a strategy that conditions future state estimation on a historical offset context; by increasing this temporal gap, we unsupervisedly disentangle static domain representations by filtering out transient properties. Second, we integrate the learned domain representations directly into the generative process by biasing the prior distribution and reformulating the diffusion target. Extensive experiments on challenging benchmarks across locomotion and manipulation demonstrate the superior performance, and the generalizability of DADP over prior methods. More visualization results are available on the https://outsider86.github.io/DomainAdaptiveDiffusionPolicy/.