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9 Multi-Modal Foundation Models, Embodied AI, and Robotics

Motivation and Scope​

Modern AI systems aim to move beyond single-modality perception (e.g., images only) toward multi-modal understanding, physical interaction, and real-world autonomy.

Multi-Modal Foundation Models​

Definition​

Multi-modal foundation models are large-scale models trained on multiple modalities, such as:

  • Vision (images, videos)
  • Language (text, instructions)
  • Audio
  • Actions / trajectories (in embodied settings)

They serve as general-purpose representations that can be adapted to many downstream tasks.

Key Characteristics​

  • Trained on massive, diverse datasets
  • Learn shared representations across modalities
  • Support zero-shot and few-shot generalization
  • Often use transformer-based architectures

Examples (Conceptual)​

  • Vision–Language Models (image captioning, VQA)
  • Text-to-image / video generation (e.g., diffusion models)
  • Models that map language instructions to actions

Computer Vision as the Perceptual Backbone​

Core Goal​

Bridge the gap between pixels and semantic meaning.

Classical Vision Tasks​

  • Recognition (objects, scenes, actions)
  • Reconstruction (3D shape, depth, geometry)
  • Generation (image and video synthesis)
  • Interaction (perception for action, especially in robotics)

Evolution​

  • Early vision: hand-crafted features, geometric models
  • Deep learning era: CNNs, large datasets (e.g., ImageNet)
  • Foundation era: large-scale, multi-modal, generative models

Embodied AI​

Definition​

Embodied AI studies intelligent agents that:

  • Are situated in an environment
  • Perceive through sensors
  • Act through physical actions
  • Learn from interaction

Key Components​

  • Observation (vision, proprioception)
  • Action (motor commands)
  • Policy (mapping observations to actions)
  • Environment dynamics

From Perception to Interaction​

Embodied AI integrates:

  • Vision (what is around me?)
  • Language (what should I do?)
  • Planning (how to achieve the goal?)
  • Control (how to execute actions?)

Typical pipeline:

  1. Goal interpretation (from language)
  2. State perception (from sensors)
  3. Subgoal decomposition
  4. Action sequencing
  5. Feedback and adaptation

Robotics and Real-World Constraints​

Challenges​

  • Partial observability
  • Long-horizon tasks
  • Diverse objects and scenes
  • Human interaction and safety
  • Sim-to-real gap

Why Robotics Is Harder than Pure Vision​

  • Errors accumulate over time
  • Actions change future observations
  • Physical constraints and uncertainty matter

Benchmarks and Simulation​

Simulation Environments​

Used to scale data collection and training:

  • Interactive 3D environments
  • Physics-based simulation
  • Diverse scenes and object configurations

Example Properties​

  • Thousands of everyday tasks
  • Ecologically realistic environments
  • Symbolic + visual task descriptions

Human-Centric Perspective​

Implications:

  • Tasks grounded in daily human activities
  • Evaluation based on usefulness, not just task success
  • Preference-aware benchmarks

Toward General-Purpose Intelligent Agents​

Ultimate objective:

  • Agents that can follow open-ended instructions
  • Adapt to new objects and environments
  • Combine perception, reasoning, and action
  • Operate autonomously in the real world

This connects:

  • Multi-modal foundation models
  • Embodied learning
  • Robotics
  • Cognitive and systems-level AI