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Physical AI & Humanoid Robotics

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Topic 1 — Foundations of Perception and Sensor Understanding

This topic explains what perception means for humanoid robots and how raw sensor data becomes structured, actionable information. It introduces the main sensor modalities (RGB, depth, LiDAR, IMU), the data types they produce, and the representations used in modern perception systems.

1.1 What Is Perception?​

Perception is the process of turning raw sensor measurements into a state estimate of the world:

  • What objects are around me?
  • Where am I relative to the environment?
  • What is moving, and how fast?
  • What can I safely interact with?

For humanoid robots, perception is the missing link between:

  • Sensors (cameras, LiDAR, IMUs) and
  • Decision-making (planning, control, language models).

Without perception, a robot can move but does not know where or why. With perception:

  • The robot can avoid obstacles instead of walking blindly.
  • It can distinguish humans from furniture.
  • It can localize objects (e.g., "the red mug") and act on language instructions.

Perception is best viewed as state estimation:

  1. Seeing — Collect sensor data (images, point clouds, IMU readings).
  2. Understanding — Infer structure (objects, surfaces, poses, maps).
  3. Acting — Use this understanding to plan and execute motion.

1.2 Vision Input Streams​

Modern humanoids typically combine several sensor modalities.

RGB Cameras​

RGB cameras capture 2D color images:

  • High information density: textures, colors, appearance.
  • Great for object recognition, segmentation, and scene understanding.
  • Sensitive to lighting changes, glare, and motion blur.

Depth Cameras (RGB-D)​

Depth cameras (like Intel RealSense) provide per-pixel distance in addition to color:

  • Each pixel has a depth value representing distance along the camera ray.
  • Enables direct 3D reconstruction of nearby surfaces.
  • Useful for:
    • Short-range obstacle detection.
    • Grasp planning (estimating object shape and position).
    • Human-robot interaction at close range.

Limitations:

  • Effective range is limited (often a few meters).
  • Struggles with reflective, transparent, or very dark surfaces.

LiDAR (Light Detection and Ranging)​

LiDAR sensors emit laser beams and measure return times to build point clouds:

  • Very accurate range measurements over longer distances.
  • Robust to lighting (works in dark environments).
  • Common in autonomous vehicles and mobile robots.

Trade-offs:

  • Less dense texture information than cameras.
  • Hardware can be more expensive and bulky.

IMU (Inertial Measurement Unit)​

IMUs measure:

  • Accelerations (linear acceleration, including gravity).
  • Angular velocities (rotational rates).

They provide:

  • Short-term motion and orientation estimates.
  • Critical input for:
    • Balancing and gait control.
    • Sensor fusion (e.g., combining IMU with camera-based VSLAM).

GPS (Optional for Outdoor Robots)​

For outdoor robots:

  • GPS provides approximate global position.
  • Often fused with IMU and other sensors in localization pipelines.

In indoor labs, GPS is usually unavailable, so robots rely on visual and inertial cues.

1.3 Data Types and Representations​

Different sensors produce different data types, which are then transformed into representations suitable for learning and planning.

Images, Tensors, and Feature Maps​

  • Image: 2D array (height × width × channels) of pixel values.
  • Tensor: Generalization of arrays; vision models treat images as tensors.
  • Feature map: The output of intermediate layers in a neural network.

Deep vision models transform images into:

  • Hierarchies of features (edges → textures → parts → objects).
  • Embeddings: Compact vector representations capturing semantic content.

These feature maps and embeddings feed into:

  • Object detectors and segmenters.
  • VLMs that link visual regions to language tokens.

Point Clouds​

A point cloud is a set of 3D points (x, y, z), often with additional attributes (intensity, color).

Produced by:

  • LiDAR.
  • Depth cameras (after projection).

Used for:

  • Obstacle detection and free-space estimation.
  • Map building and registration (aligning scans over time).
  • 3D object detection and tracking.

Voxel Grids and TSDF​

To build 3D maps, point clouds may be converted into:

  • Voxel grids: 3D grids where each cell represents occupancy or a probability.
  • TSDF (Truncated Signed Distance Function):
    • Each voxel stores the signed distance to the nearest surface.
    • Positive outside, negative inside, zero at the surface.
    • Good for generating smooth meshes.

These volumetric representations are widely used in SLAM and reconstruction.

Meshes​

Meshes approximate surfaces with vertices and faces:

  • Produced from TSDF or depth fusion.
  • Useful for visualization, collision checking, and planning.

Optical Flow and Motion Cues​

Optical flow estimates pixel motion between frames:

  • Encodes how image content moves over time.
  • Used for:
    • Tracking objects.
    • Estimating ego-motion.
    • Detecting dynamic obstacles.

Disparity maps (in stereo cameras) and motion cues provide additional depth and motion information.

1.4 Choosing 2D vs 3D Representations​

Perception pipelines must choose appropriate representations:

  • 2D-centric (images and feature maps):
    • Better for semantic understanding (what and where in image coordinates).
    • Works well for:
      • Object recognition.
      • Semantic segmentation.
      • Human pose estimation.
  • 3D-centric (point clouds, voxels, meshes):
    • Better for geometry and spatial reasoning (where in 3D space).
    • Works well for:
      • Navigation and collision avoidance.
      • Grasp planning and manipulation.
      • Map building and SLAM.

Most practical systems are hybrid:

  • Use 2D models for semantics (class labels, affordances).
  • Project or fuse results into 3D for planning (object positions in world coordinates).

1.5 Foundations for the Rest of the Chapter​

The rest of Chapter 4 builds on these foundations:

  • Topic 2 uses images and feature maps for object understanding.
  • Topic 3 uses point clouds and TSDF for mapping and SLAM.
  • Topic 4 introduces multimodal fusion, combining visual embeddings with language.
  • Topic 5 connects these representations to planning and control.

As you work through the labs, always keep in mind:

  • What representation am I using?
  • What information does it preserve or discard?
  • How will this representation be consumed by the next stage (planner, controller, or language model)?

These questions will guide you toward designing perception systems that are not only accurate, but also useful for autonomy.