Source-linked AI summary
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Haoyi Niu, Jianming Hu, Guyue Zhou, Xianyuan Zhan
TL;DR
Collecting sufficient target-domain data is costly and safety-constrained, while source domains can differ in environment and embodiment. This survey categorizes cross-domain policy-transfer methods and synthesizes their approaches and challenges. It concludes that the field has open challenges and promising future trends.
Problem
Target-domain data collection is costly and safety-constrained, while accessible source domains can differ from target environments and embodiments.
Source: §1 · p. 1Method
The survey categorizes domain gaps, reviews transfer methods, synthesizes methodological insights, and discusses open challenges and future directions.
Source: §1 · p. 1, §2.2 · p. 2, §6 · p. 8Results
The survey distinguishes appearance, viewpoint, dynamics, and morphology gaps and summarizes approaches, overarching insights, open challenges, and future trends.
Source: §6 · p. 8Takeaways & Limitations
The authors hope the survey will bring new insights and expedite research on cross-domain policy transfer for embodied agents.
Source: §1 · p. 2Source-domain manipulation depends on manipulable sources, detailed environment-modeling knowledge, and substantial computing resources, while complex target properties can remain difficult to capture in simulation.
Source: §4.1 · p. 6
Abstract
from arXiv · showhide
The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, collecting sufficient unbiased data from the target domain remains a challenge due to costly data collection processes and stringent safety requirements. Consequently, researchers often resort to data from easily accessible source domains, such as simulation and laboratory environments, for cost-effective data acquisition and rapid model iteration. Nevertheless, the environments and embodiments of these source domains can be quite different from their target domain counterparts, underscoring the need for effective cross-domain policy transfer approaches. In this paper, we conduct a systematic review of existing cross-domain policy transfer methods. Through a nuanced categorization of domain gaps, we encapsulate the overarching insights and design considerations of each problem setting. We also provide a high-level discussion about the key methodologies used in cross-domain policy transfer problems. Lastly, we summarize the open challenges that lie beyond the capabilities of current paradigms and discuss potential future directions in this field.
1 Introduction
Target-domain data collection is costly and safety-constrained, so researchers use accessible source domains despite environment and embodiment gaps. The survey organizes transfer methods by gap type, synthesizes methodological insights, and discusses open challenges and future directions.
1 Introduction
- Target-domain data collection can be costly and safety-constrained, motivating the use of accessible source-domain data despite domain gaps.Simulation and laboratory settings permit safer exploration and cheaper collection, but may not capture target-domain physical properties or embodiments.Source: §1 · p. 1
- Fragmentation across gap types, learning paradigms, data constraints, and setting assumptions makes it difficult to understand connections among transfer strategies.The survey aims to provide a holistic view of the field.Source: §1 · p. 1
- The survey organizes cross-domain policy transfer methods into appearance, viewpoint, dynamics, and morphology gap categories.It also unifies cross-domain notations and definitions, discusses connections among gap types, and synthesizes methodological insights, challenges, and future directions.Source: §1 · pp. 1–2, Figure 1 · p. 2
2 Overviews
The survey defines domains using environment, embodiment, and their resulting dynamics, then frames policy transfer in terms of target-domain performance. It distinguishes appearance, viewpoint, dynamics, and morphology gaps, including their overlaps and differences.
2.1 Notations and Definitions
- A domain comprises its environment, embodiment, and the dynamics shaped by both; a domain-dependent MDP also varies its state, action, transition, and reward information by domain.The transfer definition compares a source policy, a transferred policy, and an optimal target policy using a target-domain performance metric.Source: §2.1 · p. 2
2.2 Categorization of Domain Gaps
- Appearance gaps concern visual properties, while viewpoint gaps arise from sensor configurations; together they are sometimes called visual gaps.The survey also notes perception gaps when observations come from sensors beyond cameras, such as lidar.Source: §2.2 · p. 2
- Dynamics gaps concern differing transitions from embodiment-environment or within-embodiment interactions, whereas morphology gaps concern differing designs and can alter state and action spaces.Morphology gaps may therefore overlap with modality differences; some literature groups morphology and internal physical-property differences as embodiment gaps, distinct from morphology gaps.Source: §2.2 · pp. 2–3
3 Policy Transfer Across Different Gaps
The survey reviews transfer approaches for appearance, viewpoint, dynamics, morphology, and multiple simultaneous gaps. Across these settings, methods translate observations, align representations or states, adapt dynamics, or separate transferable task information from embodiment-specific control.
3.1 Cross-Appearance Policy Transfer
- Appearance-gap methods translate source observations toward target observations, learn shared semantic representations, or use domain randomization and visual augmentation.Image translation can use cycle consistency or real-to-sim adaptation; canonical representations and reconstruction provide alternatives, while real-to-sim adaptation may be computationally inefficient at deployment.Source: §3.1 · p. 3
3.2 Cross-Viewpoint Policy Transfer
- Viewpoint-transfer methods address camera bias through domain-confused imitation, context translation, action-aware recurrent models, contrastive learning, or hierarchical sub-goals.When demonstrations also differ in morphology, invariant features alone may not suffice; meta-learning requires separately trained models for unseen tasks.Source: §3.2 · p. 3
3.3 Cross-Dynamics Policy Transfer
- Dynamics-transfer methods modify or calibrate source dynamics, adapt policy learning for fixed sources, or align state distributions and transitions across domains.These approaches face constraints including manually specified parameter spaces, target-data demands, and imperfect state-reachability overlap between domains.Source: §3.3 · p. 4
3.4 Cross-Morphology Policy Transfer
- Morphology-transfer methods separate morphology-agnostic high-level goals from morphology-specific control or condition action generation on embodiment representations.Universal control across morphologies can be resource-intensive because the morphology representation space grows exponentially; pretraining across morphology libraries can support transfer to new morphologies and tasks.Source: §3.4 · pp. 4–5
3.5 Cross-Multi-Gap Policy Transfer
- For multiple gaps, some methods learn direct state-action correspondences, while others learn domain-invariant features, skills, or contrastive multimodal representations.Many prior works assume paired, temporally aligned data and address only one gap type; multi-gap approaches include correspondence learning from unpaired demonstrations, skill alignment, and contrastive representation learning.Source: §3.5 · p. 5
4 Discussions on Methodologies
The section groups cross-domain policy-transfer methods by how they address domain gaps, from modifying source domains to aligning representations and separating high- and low-level control. Each approach has distinct requirements or limitations.
4.1 Source Domain Manipulation
- Source-domain manipulation randomizes or calibrates environment parameters and can adapt morphology, but depends on manipulable sources, modeling knowledge, and computation.Randomization can cover visual properties and dynamics; complex target properties may remain under-modeled.Source: §4.1 · pp. 5–6
4.2 Learn Domain Correspondences / Corrections
- Correspondence and correction methods translate visual or viewpoint data, align states and actions, or adjust rewards and dynamics-based learning signals.Some designs use unpaired data or balance paired supervision with unpaired-data regularization.Source: §4.2 · p. 6
4.3 Identify Domain-Invariant Distributions
- Domain-invariant distribution methods match state visitation across domains, but often need prior state alignment and added task-progress awareness for long-horizon tasks.They typically address dynamics and morphology gaps rather than visual discrepancies.Source: §4.3 · p. 6
4.4 Learn Domain-Invariant Features
- Domain-invariant features can be reused across contexts, but may degenerate or lack temporal alignment unless paired with alignment techniques or progression-aware learning.Long-horizon, multi-skill self-supervision can yield progression-aware representations and visual rewards.Source: §4.4 · p. 6
4.5 Build Hierarchical Control Paradigms
- Hierarchical control separates domain-independent high-level skills or sub-goals from domain-specific low-level actions, while meta-learning uses context embeddings for fast adaptation.Meta-learned policies can fine-tune on small amounts of target-domain data.Source: §4.5 · pp. 6–7
5 Open Challenges and Future Trends
The section identifies open challenges in transferring policies across modalities and domains, adapting to changing target data, balancing generalization with adaptation, and evaluating transfer safely. It outlines potential directions for addressing these challenges.
5.1 Different Sensor and Actuator Modalities
- Transfer across significantly different sensor and actuator modalities remains unclear, and reliance on costly, limited expert demonstrations constrains current work.The section points to learning transferable information from non-optimal, in-the-wild demonstrations as a future direction.Source: §5.1 · p. 7
5.2 Multi-Source Data Sharing
- Correspondence methods commonly assume only two domains, while practical data-scarcity settings call for flexible use of multiple sources with varying gap scales.Potential directions include unified feature spaces or filtering and editing source data using target-domain criteria.Source: §5.2 · p. 7
5.3 Continual Target Fine-Tuning
- Continual fine-tuning could integrate target data obtained sporadically, supporting skill acquisition and policy adaptation as target domains change.The target data may arrive with different coverage and skillsets; continual learning could also relax the time-invariant-target assumption.Source: §5.3 · p. 7
5.4 Generalization and Adaptation Trade-Off
- Balancing strong adaptation to one target with generalization across random domains remains challenging, and vision-language transfer still requires representation alignment.Foundation models and extensive cross-domain data are proposed as a practical approach to the generalization-adaptation balance.Source: §5.4 · p. 7
5.5 Off-Domain Policy Evaluation
- Source-domain evaluation is faster and more controlled than target-domain evaluation but less reliable, and principled transfer criteria remain scarce.The section calls for off-domain evaluation methods and standardized real-world benchmarks using accessible sources and limited pre-collected target data.Source: §5.5 · p. 7
6 Conclusion
The survey unifies terminology and organizes cross-domain policy-transfer methods by domain-gap type, then synthesizes shared insights and open directions.
6 Conclusion
- The review distinguishes domain-gap types and clarifies their connections and differences.It categorizes methods addressing appearance, viewpoint, dynamics, and morphology gaps.Source: §6 · p. 8
- The survey summarizes shared methodological insights, open challenges, and promising future trends in cross-domain policy transfer.Source: §6 · p. 8
- The authors frame addressing these challenges as important to developing robust and versatile solutions for real-world deployment.Source: §6 · p. 8