Humanoid Robot Manipulation

The human hand is one of nature's most versatile tools. Replicating its dexterity in robots is a grand challenge that combines hardware, perception, and learning.

Grasping Fundamentals

Grasping is the foundation of manipulation. A robot must determine where to place its fingers and how much force to apply:

• Grasp planning: Computing finger positions and approach angles from visual input (point clouds, depth images)
• Force closure: Ensuring the fingers apply enough force from enough directions to hold the object securely
• Compliance: Using soft, compliant grippers that conform to object shapes rather than requiring perfect positioning

Dexterous Hands

Modern humanoid hands range from simple parallel grippers to fully articulated five-finger designs:

Learning Manipulation

Rather than programming every possible grasp, modern robots learn manipulation skills:

import torch
from diffusion_policy import DiffusionPolicy

# Diffusion Policy: learns to generate action
# sequences from demonstration data

policy = DiffusionPolicy(
    obs_dim=256,       # Visual encoder output
    action_dim=7,      # 6-DOF pose + gripper
    horizon=16,        # Predict 16 future actions
    n_diffusion_steps=100,
)

# Training: learn from human demonstrations
for batch in demo_dataloader:
    obs, actions = batch
    loss = policy.compute_loss(obs, actions)
    loss.backward()
    optimizer.step()

# Inference: generate actions from observation
actions = policy.predict(current_obs)  # Returns 16-step plan

Manipulation Skills

Key manipulation capabilities for humanoid robots:

• Pick and place: Grasping objects and placing them at target locations with precision
• Tool use: Operating screwdrivers, hammers, and other tools designed for human hands
• Bimanual manipulation: Coordinating both arms for tasks like folding laundry or opening jars
• In-hand manipulation: Rotating and repositioning objects within the fingers without setting them down
• Deformable object handling: Managing cloth, rope, and other flexible materials

Tactile Sensing

Vision alone is insufficient for manipulation. Tactile sensors provide critical information:

• Contact detection: Knowing when and where the gripper touches an object
• Force estimation: Measuring grip force to avoid crushing or dropping objects
• Slip detection: Sensing when an object is about to slip and increasing grip force
• Material recognition: Identifying object properties (hard/soft, rough/smooth) through touch