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Humanoid Robots: From Research to Real-World Applications

Overview of humanoid robots — bipedal walking, whole-body control, and platforms like Tesla Optimus, Figure 02, and Unitree H1.

Nguyen Anh Tuan1 tháng 12, 202510 min read
Humanoid Robots: From Research to Real-World Applications

What is a Humanoid Robot?

Over the past two years, if you've followed tech news, you've certainly seen videos of humanoid robots walking, folding clothes, or even making coffee. Humanoid robots — machines shaped like humans — are becoming the biggest tech race since smartphones.

But why do we need robots that look like humans? The answer is simple: our world is designed for humans. Stairs, door handles, keyboards, tools — everything is optimized for human bodies with two arms and two legs. A humanoid robot can operate in existing environments without requiring infrastructure changes.

Humanoid robot in research

Types of Humanoid Robots

Not all humanoids are created equal. Currently, there are 3 main types:

Primary Applications

Humanoid robots target 4 major sectors:

  1. Manufacturing: Working on production lines alongside humans, especially repetitive or dangerous tasks
  2. Logistics & Warehouse: Pick-and-place, goods transport in warehouses
  3. Healthcare: Patient assistance, medical supply transport in hospitals
  4. Household: Long-term goal — household helpers, elderly care

Leading Humanoid Platforms 2024-2025

The humanoid race is hotter than ever. These are the most notable players.

Tesla Optimus Gen 2

Elon Musk is betting big on humanoids. Optimus Gen 2 (launched late 2024) shows significant improvements over Gen 1:

Tesla's strength: they have factories for immediate deployment (Gigafactory) and massive AI training infrastructure. Weakness: hardware not as mature as robotics specialists.

Figure 02

Figure AI shocked the market with OpenAI partnership and investments from Jeff Bezos and Microsoft. Figure 02 is the second generation:

Figure 02's demo video talking to humans and handing an apple on request went viral — and it's not CGI.

Unitree H1 and G1

While Tesla and Figure target enterprise, Unitree (China) is democratizing humanoid robotics:

Unitree is doing for humanoids what DJI did for drones — making cutting-edge technology accessible.

Boston Dynamics Atlas (Electric)

Boston Dynamics retired the legendary Atlas hydraulic and launched an entirely new electric Atlas in April 2024:

1X NEO

Norwegian startup 1X Technologies takes a different approach — focusing on household applications:

AI technology in robotics

Core Technical Challenges

This is where it gets interesting for engineers. Humanoid robots represent an incredibly complex integration challenge — combining mechanics, electronics, and AI at the highest level.

Bipedal Locomotion

Walking on two legs sounds simple but is one of robotics' hardest problems. Humans take ~1 year to learn to walk, and our brains handle this unconsciously with millions of neurons.

Traditional approaches:

# Simplified LIPM - calculate CoM position over time
import numpy as np

g = 9.81  # gravity
z_c = 0.8  # CoM height (meters)
omega = np.sqrt(g / z_c)  # natural frequency

def lipm_trajectory(x0, xdot0, t):
    """Linear Inverted Pendulum Model trajectory"""
    x = x0 * np.cosh(omega * t) + (xdot0 / omega) * np.sinh(omega * t)
    xdot = x0 * omega * np.sinh(omega * t) + xdot0 * np.cosh(omega * t)
    return x, xdot

Modern approach — Reinforcement Learning:

The new trend is using RL to learn walking policies end-to-end. Robots train in simulation (usually Isaac Sim or MuJoCo), then transfer to real world — a technique called sim-to-real transfer.

# Example reward function for bipedal walking (simplified)
def compute_reward(robot_state):
    # Reward forward velocity
    forward_vel_reward = robot_state.linear_velocity[0] * 2.0
    
    # Penalize energy consumption
    energy_penalty = -0.01 * np.sum(np.abs(robot_state.joint_torques))
    
    # Penalize excessive body tilt
    orientation_penalty = -5.0 * np.abs(robot_state.body_roll)
    
    # Reward staying alive (not falling)
    alive_bonus = 1.0
    
    return forward_vel_reward + energy_penalty + orientation_penalty + alive_bonus

Agility Robotics (Digit) and Unitree both use RL for locomotion. Results: robots walk more naturally, recover from pushes, and adapt to uneven terrain.

Whole-Body Control

When a robot walks and manipulates simultaneously, you cannot control legs and arms independently — everything affects dynamics. Whole-body control solves this.

Task-space control: Instead of controlling individual joints, define tasks in Cartesian space (e.g., "right arm holds a cup at position xyz") and solvers find optimal joint angles.

Multi-contact dynamics: When the robot contacts multiple points (2 feet + 1 arm touching a table), calculate contact forces satisfying friction cone constraints. This is a quadratic programming (QP) problem solved in real-time at 1kHz.

# Conceptual whole-body QP formulation
# minimize: ||J_task @ qddot - task_acceleration||^2 + w * ||qddot||^2
# subject to:
#   M @ qddot + C @ qdot + g = S.T @ tau + J_contact.T @ f_contact
#   friction_cone(f_contact)  # Coulomb friction
#   joint_limits(q, qdot, qddot)
#   torque_limits(tau)

Dexterous Manipulation

Grasping objects is a major challenge. The human hand has 27 degrees of freedom and thousands of tactile receptors. Robotic hands are approaching this capability:

Wheeled Humanoid — Pragmatic Solution

Two-legged walking isn't always optimal. In warehouses and factories:

Wheeled humanoids combine humanoid's flexible upper body (dual-arm manipulation) with mobile robot's efficient lower body. This may be the winning form factor for industrial applications before bipedal humanoids mature.

Software Stack

If you want to start with humanoid robotics, this is the most common software stack.

ROS 2 + MoveIt2

ROS 2 (Robot Operating System) remains the de facto standard for robotics. MoveIt2 is the motion planning framework:

# Install MoveIt2 for ROS 2 Humble
sudo apt install ros-humble-moveit

# Launch motion planning demo with humanoid robot
ros2 launch humanoid_moveit_config demo.launch.py

MoveIt2 provides:

NVIDIA Isaac Sim — Sim-to-Real

Isaac Sim is a game-changer for humanoid development:

# Isaac Sim - create humanoid training environment (conceptual)
from omni.isaac.lab.envs import ManagerBasedRLEnv

env_cfg = HumanoidWalkingEnvCfg()
env_cfg.scene.num_envs = 4096  # 4096 robots in parallel
env_cfg.rewards.forward_velocity.weight = 2.0
env_cfg.rewards.energy_consumption.weight = -0.01

env = ManagerBasedRLEnv(cfg=env_cfg)
# Train with PPO in a few hours → robot learns to walk

Foundation Models for Manipulation

Instead of training models for each task, foundation models enable robots to learn manipulation from diverse data:

# Use Octo for manipulation (simplified)
from octo.model.octo_model import OctoModel

model = OctoModel.load_pretrained("hf://rail-berkeley/octo-base-1.5")

# Predict action from observation + language instruction
task = model.create_tasks(texts=["Pick up the red cup"])
action = model.sample_actions(
    observations={"image_primary": camera_image},
    tasks=task,
    rng=jax.random.PRNGKey(0)
)
# action = [dx, dy, dz, droll, dpitch, dyaw, gripper]

Robot in real-world applications

Vietnam Market: Opportunities and Challenges

Opportunities

Vietnam occupies an interesting position in the humanoid revolution:

Challenges

Near-term Application Potential (2025-2028)

  1. Wheeled humanoid in warehouses: Pick-and-place for e-commerce fulfillment. Fastest ROI.
  2. Upper-body humanoid in manufacturing: Mounted on mobile base, performing assembly tasks.
  3. Service robots in hospitality: Hotels, restaurants — Vietnam has a large tourism sector.

Long-term (2028+), as humanoid prices drop below $20K (Unitree is pursuing this), expect humanoid adoption in healthcare and households.

Next Steps

Humanoid robotics is at an inflection point — like smartphones in 2007. Technology is mature enough, prices are dropping fast, and use cases are becoming clear. In 5 years, humanoid robots will transition from cool demos to real production tools.

To learn more:

Humanoid robots are more than machines — they're the physical interface between AI and the real world. And the game is just beginning.

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