Large Model Empowered Embodied AI

• Large model empowered embodied AI is defined as the integration of high-capacity language, vision, and world models with physically instantiated agents to improve perception, planning, and control.
• Hierarchical and end-to-end paradigms leverage multimodal tokenization, transformer-based fusion, and internal simulation for adaptive decision-making.
• System challenges like runtime latency, memory management, and data scarcity drive ongoing research into efficient control and robust multi-agent coordination.

Large model empowered embodied AI refers to the integration of high-capacity, generalist models—predominantly LLMs, vision-LLMs (VLMs), and world models (WMs)—with physically instantiated agents capable of perception, decision-making, action, and continual learning. Over the past several years, advances in model capacity, cross-modal fusion, world model construction, and planning algorithms have positioned such large models at the core of autonomous and adaptive embodied systems. This paradigm spans hierarchical and end-to-end learning, imitation and reinforcement learning with generative models, and the synergistic unification of verbal, visual, spatial, and physical reasoning. The following sections present a technical synthesis of the key architectures, methodologies, learning paradigms, current limitations, and future trajectories, as documented in the contemporary literature.

1. Foundations and Paradigms of Large Model Empowered Embodied AI

Large model integration in embodied AI is organized into two principal decision-making paradigms: hierarchical and end-to-end (Liang et al., 14 Aug 2025).

Hierarchical Paradigms

Hierarchical approaches decompose the embodied pipeline into discrete modules:

• Perception: Sensory input (vision, language, proprioception) is parsed, often using pretrained multimodal backbones.
• High-level Planning: LLMs are employed to generate structured plans in Planning Domain Definition Language (PDDL), natural language, or code. This enables zero/few-shot task decomposition, subgoal generation, and constraint satisfaction.
• Low-level Execution: Subgoals are mapped to control actions. Execution modules can be modular (reinforcement learning agents, imitation-learned controllers), occasionally guided by outputs from large models.
• Feedback and Self-reflection: The decision process involves iterative refinement; after acting, the model may re-prompt itself or seek human/environmental feedback to correct errors.

This structure supports flexible, interpretable planning and enables dynamic adjustment in open-domain, long-horizon tasks.

End-to-End Vision-Language-Action (VLA) Paradigms

End-to-end methods rely on direct mapping from perception and instruction to action:

• Tokenization: Vision (image/video), language (instruction), proprioception, and sometimes even action states are encoded as discrete or continuous “tokens.”
• Fusion and Decoding: Attention-based transformers (VLA models) and cross-modal fusion transmit the tokenized data to an autoregressive decoder. The decoder produces action tokens, which are subsequently detokenized into command signals.
• Direct Policy Learning: This structure supports high-capacity training and allows for joint reasoning over all input modalities.

Large-scale VLA architectures are exemplified by models such as RT-1/RT-2 (Liu et al., 9 Jul 2024), and they benefit from large, internet-scale pretraining followed by fine-tuning on robot demonstrations.

2. World Models and Internal Simulation

One of the most significant developments is the incorporation of world models—internal simulators that predict environment dynamics—to enhance decision-making and training efficiency (Liang et al., 14 Aug 2025, Liu et al., 9 Jul 2024). These can be categorized as:

• Latent Space World Models: Models like Dreamer and PlaNet use recurrent state-space models (RSSMs) to encode observations into latent representations, allowing the agent to “dream” future trajectories via latent rollouts.
• Transformer-Based World Models: Transformer architectures (e.g., IRIS, Genie) with self-attention allow for modeling long temporal dependencies, crucial for memory-driven planning and reasoning.
• Diffusion-Based World Models: Diffusion models (e.g., Sora) learn to synthesize future frames by iterative denoising of image/video tokens, supporting high-fidelity visual prediction.
• Joint Embedding Predictive Architectures (JEPA): Emphasize semantic embedding spaces for common-sense world modeling, supporting counterfactual and causal reasoning beyond pixel-level transitions.

World models serve three primary roles: prospective simulation of plans, synthetic data generation for learning, and context augmentation for robust decision-making.

3. Embodied Learning: Imitation and Reinforcement with Large Models

Imitation Learning

Behavior cloning is formalized as standard supervised learning, with objective:

$$\mathcal{L}(\pi) = -\mathbb{E}_{\tau \sim D}[\log \pi(a|s)]$$

where $D$ is the demonstration dataset. Large models enhance this via:

• Diffusion Policies: Iterative noise injection and removal enable modeling diverse and robust action sequences, aiding in long-horizon, multimodal behaviors.
• Transformer Policy Networks: Viewing trajectories as sequences permits modeling of dependencies over hundreds of timesteps, suited to complex tasks.
• Pretrained Multimodal Fusion: VLMs align vision and state with language, improving generalization and transfer (Mu et al., 2023).

Reinforcement Learning

Goal-conditioned RL is defined by:

$$\mathcal{J}(\pi) = \mathbb{E}_{\pi, T, O}\left[ \sum_{t=0}^\infty \gamma^t R(s, a, g) \right]$$

with discount factor $\gamma$ and goal $g$. Large models contribute by:

• LLM-Generated Rewards: LLMs can synthesize reward functions from description (e.g., Text2Reward, Eureka), automating reward engineering (Liang et al., 14 Aug 2025).
• Expressive Policy Models: Diffusion, transformer, and language-conditioned policies capture richer action structure and higher-level semantics.
• Offline and Data-Constrained Settings: Transformer-based architectures are better suited for learning from limited real-world data.
• Policy Reflection and Adaptive Planning: LLMs and world models can analyze errors post-hoc and recalibrate plans, increasing robustness in non-stationary or partially observed domains.

4. Model Architectures, Data, and Benchmarks

Several benchmark models embody these principles:

• RoboBrain 2.0 (Team et al., 2 Jul 2025) features a vision encoder (windowed, multi-resolution, with multi-dimensional rotary positional encodings), an MLP projector, and a decoder-only LLM backbone. Inputs include spatial (image, video, multi-view) and temporal (trajectory, action sequence) data, producing outputs for spatial localization, affordance prediction, and closed-loop planning. State-of-the-art metrics are reported on BLINK, RefSpatial-Bench, EgoPlan2, and RoboBench.
• EmbodiedGPT (Mu et al., 2023) fuses a ViT-based visual encoder with a LLaMA-7B LLM, bridged by learnable embodied query embeddings (“Embodied-former”), and incorporates prefix-tuning for efficient domain adaptation. Plans are generated as hierarchical, chain-of-thought outputs using large annotated egocentric datasets (EgoCOT).
• RT-2, RT-H and related MLMs (Liu et al., 9 Jul 2024) integrate vision, language, and action planning, using joint tokenization and transformer architectures.
• World model–augmented VLMs enable token-based representations for both spatial and temporal prediction.

Training paradigms involve multi-stage curricula—initially on open-domain multimodal data, then on robot- or environment-specific datasets, and finally with chain-of-thought or reinforcement-driven refinement (Team et al., 2 Jul 2025).

5. System-Level Challenges and Optimization Strategies

Despite advanced capabilities, the integration of large models in embodied AI introduces salient system-level obstacles (Wan et al., 26 Apr 2025, Liang et al., 14 Aug 2025):

• Runtime Latency: LLM-dominated planning introduces high stepwise latency (10–30 s/timestep), with planning/communication consuming over 70% of runtime.
• Collaboration and Scalability: In multi-agent systems, prompt length grows quadratically with agent number in decentralized settings, straining LLM context windows and degrading performance. Centralized planning scales linearly in latency but suffers in decision quality as agent count rises (Wan et al., 26 Apr 2025).
• Memory and Reflection: Disabling or degrading memory/retrieval modules or reflective reasoning sharply lowers task success rates and increases cycle count, emphasizing the importance of robust memory integration.
• Prompt Length Explosion: Redundant communication and uncurated memory lead to token bloat, diluting key details and degrading inference quality.
• Low-Level Control: LLMs perform poorly at direct sensorimotor control; hybrid architectures are necessary to couple high-level reasoning with task-specific controllers or trajectory optimization modules.

Optimization approaches include lighter local LLMs with selective trade-offs in reasoning, dual-memory architectures (short- and long-term store), hierarchical agent grouping, inter-module token management, and prompt structuring to minimize redundant LLM calls.

6. Core Research Challenges and Prospective Directions

Despite demonstrated improvements, several research challenges persist, shaping the future of the field (Liu et al., 9 Jul 2024, Liang et al., 14 Aug 2025, Feng et al., 8 May 2025):

• Data Scarcity and Sim-to-Real Transfer: Embodied data for physical agents is limited and expensive to collect. World models, synthetic data generation, and cross-domain adaptation are crucial ongoing areas of research.
• Continual and Lifelong Learning: Preventing catastrophic forgetting and enabling experience accumulation in open environments remains an open algorithmic challenge.
• Computational and Deployment Efficiency: Large models require significant resources, impeding real-time control and on-device deployment. Research is ongoing into parameter-efficient tuning (LoRA, adapters), model compression, and specialized hardware.
• Causal and Counterfactual Reasoning: Moving from statistical correlation to explicit modeling of physical/causal dynamics (counterfactual “what-if” reasoning) is essential for reliable task execution, safety, and adaptivity.
• Unified Benchmarks and Evaluation: Current benchmarks are fragmented and lack coverage across perception, reasoning, control, and multi-agent collaboration. Efforts are underway to establish standard, large-scale evaluation suites.

A plausible implication is that next-generation embodied AI will devote increased attention to multi-agent coordination, asynchronous and decentralized inference, robust multi-modal world modeling, and tighter integration of language-driven symbolic reasoning with embodied sensorimotor learning—all underpinned by compositional memory architectures and ongoing, data-efficient adaptation.

7. Summary Table: Large Model Roles Across the Embodied AI Stack

Subsystem	Large Model Integration	Example Methods
Perception	VLMs for multimodal fusion, grounding	ViT-based encoders, CLIP
Planning	LLMs for task decomposition, self-reflection	Chain-of-thought, PDDL generation
World Modeling	Diffusion/transformer-based simulators	Dreamer, Sora, JEPA
Control	Transformer/diffusion-based policy nets	RT-1/RT-2, diffusion policies
Feedback	LLM/CoT for self-correction, synthetic reward	Self-reflection, Text2Reward

This tabulation illustrates how large models permeate the embodied AI stack, delivering flexible reasoning and rich multimodal representation, while raising new efficiency and integration challenges.

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In summary, large model empowered embodied AI unifies LLMs, VLMs, world models, and sequence models to advance perception, planning, action, and learning in physically instantiated agents. These techniques now support both modular and end-to-end decision paradigms, internal simulation for efficient learning, and multimodal reasoning, while ongoing research addresses deployment, efficiency, and generalization hurdles fundamental to the long-term progress of embodied intelligence (Liang et al., 14 Aug 2025, Liu et al., 9 Jul 2024, Wan et al., 26 Apr 2025, Team et al., 2 Jul 2025, Mu et al., 2023).