Omni-Modal Goal Conditioning

• Omni-modal goal conditioning is a framework where agents interpret and act on goals specified via heterogeneous modalities like images, coordinates, and language.
• It employs modality-specific encoders fused into a shared latent space, enabling models to generalize and respond to diverse, real-world instructions.
• Empirical results demonstrate that omni-modal agents outperform single-modality baselines in success rates and adaptability across complex tasks.

Omni-modal goal conditioning is a paradigm in which intelligent agents (typically robotic or embodied AI systems) learn and act under goal specifications formulated in a broad spectrum of modalities—such as visual references, spatial coordinates, and natural language—within a unified architecture. The aim is to enable a single model to interpret, compose, and act upon heterogeneous goal inputs that may be presented individually or in combination. This comprehensive approach stands in contrast to most conventional methods, which are limited to a narrow modality (e.g., only coordinates or only images), and positions omni-modal goal conditioning as a foundation for adaptive, generalizable, and user-friendly autonomous systems.

1. Conceptual Foundations and Motivation

Omni-modal goal conditioning addresses a central gap in both classic and contemporary embodied AI: the lack of flexibility in goal specification for real-world agents. Early robotics and navigation policies typically accepted only coordinates or low-dimensional state vectors as goals, requiring either programmatic environment access or brittle user interfaces. However, in practical applications such as domestic robotics, human users may specify goals with a photograph (“go here”), a natural language instruction (“bring me my mug from the kitchen”), a spatial tag (coordinates on a map), or any combination thereof.

Contemporary research has shifted toward architectures that explicitly support conditioning on such flexible, multimodal specifications (Khanna et al., 9 Apr 2024, Li et al., 26 Aug 2024, Hirose et al., 23 Sep 2025). The motivation is twofold:

• Interoperability: Agents must interface with diverse human and environmental inputs.
• Generalization: Models robust to multi-modal goals tend to generalize better in new environments and tasks due to richer abstract representations and exposure to broader data distributions.

By projecting the various goal modalities into a shared, model-compatible token or embedding space, a single policy network can flexibly adapt to diverse instructions and richer user queries, thus laying the groundwork for scalable robotic foundation models.

2. Modalities and Fusion Mechanisms

Research in omni-modal goal conditioning operationalizes “goal modalities” as any input (or combination of inputs) specifying a desired agent outcome, including:

• Egocentric images: Visual goal references (e.g., a snapshot of the target location or object).
• 2D/3D spatial coordinates: Absolute or relative position goals, often used in outdoor or map-based navigation.
• Natural language: Rich, semantics-laden instructions that may contain both destination and behavioral constraints.
• Sensorimotor signals: In some works, proprioceptive signals or short demonstrations are also viewed as goal modalities.

Mechanistically, goal modalities are encoded by specialized submodules (e.g., image encoders, text encoders, coordinate MLPs) and then projected into a shared latent (token) space (Hirose et al., 23 Sep 2025). During each training step, recent frameworks (e.g., OmniVLA) employ a randomized modality fusion strategy: the model is presented with a randomly selected subset of goal modalities, with attention masking used to ensure it cannot trivially memorize a single input form. This “dropout-like” fusion mechanism forces the model to robustly extract and relate information from any available channel.

In more advanced frameworks, combinations of modalities are permitted, allowing a user to simultaneously specify, for example, a 2D goal coordinate to anchor location and natural language to instruct behavioral nuance.

3. Model Architectures and Training Strategies

Omni-modal goal conditioning is typically realized atop high-capacity Vision-Language-Action (VLA) models or transformer-based policies (Hirose et al., 23 Sep 2025). The canonical design includes:

• A visual encoder (e.g., ConvNet or Vision Transformer) for current-state perception.
• Goal-specific encoders for each modality (image, language, coordinate).
• Tokenization and projection: All goal encodings are mapped to a unified token space and fused with current observations.
• A large pre-trained language or multi-modal transformer backbone that processes these sequences and passes them to an action head (e.g., for navigation, locomotion, or manipulation).

Training leverages large, heterogeneously annotated datasets, often across many robotic platforms and domains (spanning thousands of hours and numerous environments). The key innovation is randomized modality dropout/fusion, ensuring: (1) models never overspecialize on a single goal type and (2) they learn correlations and trade-offs among modalities. The supervised objective is usually a combination of mean squared error (for action regression), object proximity (for semantic goal-reaching), and smoothness regularization: $$\min_\theta J = J_\text{il} + m_\text{obj} \cdot J_\text{obj} + J_\text{sm}$$ where $J_\text{il}$ is the imitation loss, $J_\text{obj}$ regularizes endpoint proximity to language- or image-specified targets, and $J_\text{sm}$ ensures action smoothness (Hirose et al., 23 Sep 2025).

Edge-optimized variants (e.g., OmniVLA-edge) distill this structure into smaller, resource-constrained transformers while preserving omni-modal conditioning capability.

4. Empirical Performance and Evaluation

Quantitative results consistently show that omni-modal goal-conditioned agents outperform single-modality baselines across standard testbeds (Khanna et al., 9 Apr 2024, Li et al., 26 Aug 2024, Hirose et al., 23 Sep 2025). On benchmarks that require following out-of-distribution goal prompts or combining multiple signals (e.g., “go to this location and follow this instruction”), omni-modal models show:

• Higher success rates: Both partial (progress toward goal) and complete (goal reached) metrics improve.
• Robustness to modality scarcity: When some goals are missing (e.g., language is absent or images are degraded), the agent remains functional.
• Generalization: Models pre-trained on mixed modalities adapt efficiently to new environments, hardware, and goal forms.

Evaluations on large-scale datasets (e.g., over 9,500 hours across heterogeneous robot fleets) confirm these gains, and ablations demonstrate that removing modality-fusion mechanisms or omitting modalities during training leads to reduced performance and brittleness.

In addition to standard navigation tasks, such architectures have been deployed in lifelong or multi-goal evaluation schemes (e.g., GOAT-Bench (Khanna et al., 9 Apr 2024)), where the agent must persistently recall and execute a sequence of multimodal goals across an episode, leveraging both explicit and implicit scene memories.

5. Applications, Benefits, and Limitations

Omni-modal goal conditioning is immediately relevant to:

• Robotic navigation in diverse, dynamically specified environments (e.g., receiving any combination of “go here,” “find this object,” or “reach these coordinates”).
• Human-robot interaction where natural instructions, sketches, or references must be flexibly understood.
• Data-efficient transfer learning, since scalably pre-trained agents with omni-modal heads can be rapidly adapted to new modalities or embodied tasks.
• Foundation model development for robotics and general embodied intelligence.

The fusion of modalities also makes such agents robust in real-world situations where one goal specification may be unavailable, ambiguous, or noisy. However, a plausible implication is that increasing the diversity of input modalities and their combinations imposes significant demands on data scale, encoder design, and representation alignment. Ensuring consistent behavior with partially specified or conflicting modalities remains an ongoing challenge.

6. Future Directions

The current trajectory of omni-modal goal conditioning research points toward several developmental frontiers:

• Expansion to additional modalities: Future models will likely incorporate not just images, language, and coordinates but also signals such as audio, depth, haptics, and symbolic sketches.
• Scalable foundation models: Approaches analogous to large vision-LLMs will be tuned for embodied settings, exploiting cross-modal pre-training at global scale (Zhang et al., 13 Jun 2024).
• Dynamic modality integration: Real-time switching and weighting of available goal modalities based on user preferences or environmental context may yield more robust and intuitive interfaces.
• Improved compositionality and reasoning: More advanced logic over goal modalities (e.g., following instructions conditional on scene context, or inferring implicit sub-goals from combined specifications).
• Unified evaluation and benchmarking: Systematic, open benchmarks (such as GOAT-Bench) will accelerate the establishment of objective metrics and support comparison across task domains.

In summary, omni-modal goal conditioning establishes a robust framework and set of methodologies for agents to flexibly interpret and pursue goals expressed in diverse, composable modalities. The approach advances the development of adaptable, user-aligned intelligent systems and forms a cornerstone of ongoing research in scalable robotic foundation models (Hirose et al., 23 Sep 2025).