Training Strategies for Large-Scale Multimodal Models in Embodied Intelligent Robots

By ihsumlee , 6 April 2026
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Training Strategies for Large-Scale Multimodal Models in Embodied Intelligent Robots

Concept Overview

Training strategy is a key factor in determining how a multimodal model learns to map language, vision, and robot states into actions. In embodied intelligent robots, training is not only about improving prediction accuracy, but also about balancing generalization, data efficiency, computational cost, and deployment complexity. This section introduces three major training strategies: end-to-end multimodal training, pre-training with fine-tuning, and hybrid or enhanced training methods such as mixture of experts (MoE).

Key Idea

Different training strategies reflect different assumptions about how multimodal robot intelligence should be learned:

  • End-to-end training learns task behavior directly from multimodal inputs
  • Pre-training and fine-tuning first learn general knowledge, then adapt to robot tasks
  • Hybrid or MoE-based training dynamically allocates different expert modules to different tasks

These strategies differ in data requirements, model flexibility, efficiency, and engineering difficulty.

Main Training Strategies

1. End-to-End Multimodal Training

Principle

All modality inputs are fed directly into a single model, and the system is trained to optimize the final output end-to-end.

Technical Details

  • Input

    • Language instructions
    • Image frames
    • Robot states

  • Model

    • Transformer-based multimodal encoder and decoder

  • Output

    • Continuous action trajectories
    • Discrete control commands

Advantages

  • High model consistency
  • Direct optimization of task performance

Disadvantages

  • Requires large amounts of labeled data
  • High training cost

Example

  • PaLM-E: a Google multimodal embodied model that enables robots to complete grasping tasks based on language instructions

2. Pre-Training and Fine-Tuning

Principle

The model is first pre-trained on large-scale general datasets and then fine-tuned using a smaller amount of robot-specific data.

Technical Details

  • Pre-training phase
    • Use public or large-scale data to learn language-vision associations
  • Fine-tuning phase
    • Introduce robot action data to adapt the model output to embodied tasks

Advantages

  • Strong generalization ability
  • Lower robot-data requirement during fine-tuning

Disadvantages

  • Very high computational cost during pre-training
  • Fine-tuning performance may be limited by biases in the initial model

Example

  • GR00T N1: an NVIDIA model pre-trained at large scale and adapted to cross-task robot generalization through fine-tuning

3. Hybrid and Enhanced Training

Principle

This strategy combines multimodal learning with advanced mechanisms such as mixture of experts (MoE) or reinforcement-based methods. The system dynamically selects sub-models or expert modules according to the current task.

Technical Details

  • The model contains multiple expert modules
  • A gating network assigns tasks to suitable experts
  • Multimodal inputs are processed by shared components and then routed to specialized modules

Advantages

  • High computational efficiency
  • Adaptable to diverse tasks

Disadvantages

  • Complex architecture
  • Difficult to debug and deploy

Example

  • GO-1: a vision-language-latent-action architecture that dynamically processes VLA mapping through specialized modules

Why This Matters for Embodied Intelligent Robots

Embodied intelligent robots must connect perception, language understanding, and control into a unified training framework. Different strategies are suitable for different goals:

  • End-to-end training is useful when sufficient robot data is available and direct task optimization is needed
  • Pre-training and fine-tuning is useful when large-scale general knowledge is important and robot-specific data is limited
  • Hybrid training is useful when the robot must handle multiple tasks, skills, or environments efficiently

In practice, modern embodied AI systems often combine these ideas rather than relying on only one strategy.

Summary

There are three major training strategies for multimodal embodied models: end-to-end training, pre-training with fine-tuning, and hybrid or enhanced training such as MoE-based methods. Each strategy has its own strengths and weaknesses. Choosing the right training strategy depends on data scale, task diversity, computational resources, and deployment requirements.

Common Mistakes

  • Assuming end-to-end training is always the best choice
  • Ignoring the cost and bias issues of pre-training
  • Using complex hybrid architectures without enough engineering support
  • Focusing only on model accuracy while neglecting deployment difficulty`

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