Cross-Embodied Navigation

Updated 27 October 2025

Cross-Embodied Navigation is the development of unified strategies that enable agents to navigate across varied robot forms and sensor modalities.
It leverages transformer-based tokenization, modular memory frameworks, and multi-modal datasets to standardize diverse action and observation spaces.
Empirical results demonstrate enhanced zero-shot transfer, improved efficiency, and reduced need for embodiment-specific tuning in real-world applications.

Cross-embodied navigation is the problem of designing agents or policies that can generalize navigation behaviors across multiple, diverse robot embodiments—such as wheeled robots, quadrupeds, drones, manipulators, and even simulated or GUI-based agents. Rather than developing navigation solutions for a single fixed platform, cross-embodied navigation focuses on learning unified representations, models, or policies that work across radically different physical forms, sensor suites, and control regimes. It is motivated by the demand for scalable, efficient robotics solutions and the limitations of embodiment-specific development. Recent advances leverage giant multimodal datasets, transformer architectures, modular memory or reasoning frameworks, and carefully constructed action/observation spaces to make progress toward this goal.

1. Key Concepts and Definitions

At its core, cross-embodied navigation targets the generalization of navigation skills—interpreting goals, planning, and executing low-level movement—in a manner robust to variations in robot morphology, sensor modality (images, depth, proprioception, audio), actuator diversity, and environment. Instead of optimizing for a single agent’s embodiment, the aim is to create architectures and training regimes where knowledge acquired by one robot can be leveraged or transferred efficiently to others, sometimes without further fine-tuning ("zero-shot" transfer).

The distinction between cross-task (e.g., manipulation, navigation, driving) and cross-embodiment transfer is subtle but critical. Some recent work unifies both, considering goal-conditioned policies that simultaneously span platform and task boundaries (Yang et al., 29 Feb 2024, Doshi et al., 21 Aug 2024).

Key Terms

Embodiment: The specific physical realization of an agent, including its kinematics, sensors, actuators, and control interfaces.
Cross-embodied transfer: The ability of a model to function or be adapted efficiently on embodiments different from those observed during training.
Unified policy/model: A single neural network or control policy applicable to multiple embodiments without manual architectural changes or explicit per-embodiment data alignment.

2. Model Architectures and Action/Observation Alignment

A principal challenge in cross-embodied navigation is the disparity of action and observation spaces among robot types. Various solutions appear in recent literature:

Tokenization and Transformer-based Policies: CrossFormer (Doshi et al., 21 Aug 2024) and NavFoM (Zhang et al., 15 Sep 2025) convert heterogeneous sensory and control signals from distinct robots into standardized token sequences. Special identifier tokens, readout tokens, and modular output heads ensure the transformer architecture can interface with multiple sensors and actuators, supporting action chunking for different control frequencies.
Goal-conditioned Policies with Manual Alignment: Frameworks such as (Yang et al., 29 Feb 2024) explicitly normalize and align action coordinate frames (e.g., flipping or reordering axes) across datasets to ensure a "left" command leads to similar egocentric camera transformations regardless of physical embodiment.
Two-stage IL-then-RL Decoupling: CE-Nav (Yang et al., 27 Sep 2025) employs an imitation-learned geometric General Expert (VelFlow) that learns kinematically-sound, multi-modal velocity commands agnostic to embodiment, followed by a lightweight, per-embodiment Dynamics-Aware Refiner trained by reinforcement learning to adapt reference actions to specific platform dynamics.
Parse-and-Query Modular Encoders: Vienna (Wang et al., 2022) and P3Nav (Zhong et al., 24 Mar 2025) use modular transformer architectures where inputs from multiple modalities (vision, audio, instructions, proprioception) are parsed into task- and embodiment-specific embeddings, which then query shared context memory for robust action selection.
Budget-aware Token Sampling: Real robots in NavFoM (Zhang et al., 15 Sep 2025) may produce long histories of observations. The BATS (Budget-Aware Temporal Sampling) strategy maintains recent and critical history while staying within token count limits, which is essential for accommodating diverse camera setups and long navigation horizons.

Model / Approach	Sensor/Action Alignment	Generalization Mechanism
CrossFormer (Doshi et al., 21 Aug 2024)	Tokenization, readout tokens	Unified transformer, action heads
CE-Nav (Yang et al., 27 Sep 2025)	Universal geometric expert + RL refiner	Multi-modal normalizing flow prior
Goal-conditioned policy (Yang et al., 29 Feb 2024)	Manual coordinate normalization	Diffusion model, goal image
X-Nav (Wang et al., 19 Jul 2025)	Unified proprioception + action chunking	Transformer, imitation learning
Vienna (Wang et al., 2022)	Task-embedding + parse-and-query	Shared transformer encoder

3. Training Regimes and Data Diversity

The scalability of cross-embodied navigation critically depends on the diversity and balance of data used at training.

Large-Scale Multi-Embodiment Datasets

CrossFormer is trained using 900k trajectories from 20 different embodiments, comprising single and dual-arm manipulators, wheeled robots, drones, and quadrupeds (Doshi et al., 21 Aug 2024).
NavFoM was trained on eight million navigation samples encompassing quadrupeds, drones, wheeled robots, and vehicles, and spanning tasks such as VLN, object goal navigation, and autonomous driving (Zhang et al., 15 Sep 2025).
X-Nav employs thousands of randomly generated simulated embodiments for both wheeled and quadrupedal robots, enabling generalization studies on both seen and out-of-distribution robot morphologies (Wang et al., 19 Jul 2025).
Mixed-domain policies in (Yang et al., 29 Feb 2024, Luo et al., 4 Aug 2025) blend data from manipulation, navigation, and even GUI tasks, aligning their representations through explicit goal conditioning or unified MDP reformulation.

Balanced sampling and task/embodiment weighting prevent the phenomenon where overrepresented robots or tasks dominate the policy (data imbalance). Cross-domain mix training (e.g., 50% navigation, 50% manipulation) was shown to be effective for cross-embodiment transfer (Yang et al., 29 Feb 2024).

Action and Sensor Simulation

IL-then-RL frameworks such as CE-Nav generate expert trajectories entirely in simulation using kinematically-rich planners, then adapt with minimal in-environment data collection, reducing the cost of embodiment-specific supervision (Yang et al., 27 Sep 2025).

4. Performance, Generalization, and Scalability

State-of-the-art policies now approach or match specialist models on their primary target embodiments while also demonstrating significant performance gains in transfer:

Empirical Results: CrossFormer achieved a 73% average success rate across all embodiments versus 67% for single-task baselines (Doshi et al., 21 Aug 2024). Vienna demonstrated superior multitask efficiency and reduced model size compared to single-task agents (Wang et al., 2022).
Zero-shot Transfer: X-Nav general policies trained on simulation can be deployed on real-world robots (wheeled or quadrupedal) with an 85% success rate and SPL of 0.79, without additional fine-tuning (Wang et al., 19 Jul 2025).
Transfer Benefits: Training on navigation data improves manipulation task performance (20% gain), and vice versa (5–7% navigation SR gain), indicating the value of shared spatial and semantic representations (Yang et al., 29 Feb 2024).
Adaptation Efficiency: CE-Nav achieves state-of-the-art real-world navigation performance in only 6 hours of online RL adaptation, versus 50+ hours for some baselines, by leveraging the multi-modal distributional guidance of the frozen VelFlow expert (Yang et al., 27 Sep 2025).
Generalization Across Viewpoints: NavFoM achieves robust multi-view generalization using temporal-viewpoint indicator (TVI) tokens, with explicit encoding of timestamp and camera orientation (Zhang et al., 15 Sep 2025).

5. Robustness, Safety, and Open Challenges

Robust cross-embodied navigation entails not only efficiency and generalization but also safety and resilience to both environmental variation and adversarial threats:

Adversarial Vulnerabilities: Physical adversarial patches optimized using multi-view and opacity-aware strategies can reduce navigation success rates significantly (e.g., ~40% drop), indicating that deep ML-based systems may be brittle to targeted, physically realizable attacks, with cross-embodiment implications for safety-critical settings (Chen et al., 16 Sep 2024).
Physical Deployment Challenges: Noise, limited FOV, sensor misalignment, lighting changes, and embodiment-specific locomotion constraints cause stark degradation in success rates when sim-trained VLN agents are deployed on physical humanoids, quadrupeds, or wheeled robots—even with the same high-level model architecture (Wang et al., 17 Jul 2025).
Joint Optimization: There is ongoing research into jointly optimizing system modules (transition, observation, fusion, reward-policy, action) for generality, as advocated by the TOFRA framework (Xiong et al., 21 Aug 2025).
Scaling, Data, and Task Generalization: Open challenges remain in scheduling, spatial-temporal scale adaptation, system integrity (explainability, reliability), and ensuring broad data/task coverage for universal policies (Xiong et al., 21 Aug 2025).

6. Future Directions

Several avenues are actively being developed:

Truly Generalist Agents: Foundation models for navigation/tracking/driving (Zhang et al., 15 Sep 2025), generalist controllers for both manipulation and navigation (Yang et al., 29 Feb 2024, Doshi et al., 21 Aug 2024), and unified MDP or trajectory tokenization approaches (Luo et al., 4 Aug 2025, Kotar et al., 2023).
Multi-modal and Open-set Reasoning: Integration of large pretrained VLMs, explicit spatial reasoning, object affordance modeling, and hierarchical/planning modules for open-vocabulary and instruction-based navigation across embodiments (Zhang et al., 6 Aug 2025, Zhong et al., 24 Mar 2025).
Flexible Realistic Simulation and Evaluation: Platforms like VLN-PE enable benchmarking and development on physically accurate robots across diverse morphologies, facilitating robust sim-to-real evaluation (Wang et al., 17 Jul 2025).
Imaginative World Modeling and Semantic Shortcuts: Hierarchical scene graph–based world models with proactive semantic prediction (e.g., SGImagineNav (Hu et al., 9 Aug 2025)) are being explored for anticipatory exploration across complex 3D spaces.
Confidence, Explainability, and Safety Monitoring: System integrity metrics (e.g., certainty reporting, explainable decision making) are highlighted as essential for deployment across diverse mission scenarios (Xiong et al., 21 Aug 2025), particularly where embodiment variations co-occur with unpredictable real-world changes.

7. Broader Implications and Applications

Cross-embodied navigation broadens the scope of autonomous robotics to unified agents capable of operating seamlessly on arbitrary platforms with minimal per-device tuning. Potential applications include:

Universal service robots and delivery agents adaptable to new hardware platforms with limited data.
Rescue or environmental exploration robots with heterogeneous fleets coordinating tasks based on shared models.
Industrial, commercial, or domestic systems deploying the same policy to stationary manipulators, mobile bases, and drones.
Cross-domain agents handling both real-world navigation and digital environments, with consistent policies spanning GUI and physical tasks.

The field is actively advancing toward robust, truly universal navigation agents, with ongoing research addressing outstanding challenges in scalability, robustness, sim-to-real transfer, and multi-modal integration.