Humanoid Robot Locomotion

Bipedal walking is one of the hardest problems in robotics - humans do it effortlessly, but it requires solving balance, dynamics, and terrain adaptation simultaneously.

The Challenge of Bipedal Walking

Bipedal walking is inherently unstable. A two-legged robot is essentially a controlled fall - constantly tipping forward and catching itself with the next step. This requires:

• Dynamic balance: Maintaining the center of mass over a small support polygon (the feet)
• Gait generation: Coordinating joint angles and timing for smooth, efficient walking
• Impact absorption: Handling the forces when a foot strikes the ground
• Adaptation: Adjusting to uneven terrain, slopes, and external disturbances

Classical Control Approaches

Traditional locomotion uses model-based control:

RL-Based Locomotion

Modern humanoid robots increasingly use reinforcement learning for locomotion. The policy network maps sensor observations to joint torques:

import torch
from isaacgym import gymapi

# Observation space: joint positions, velocities, IMU data
# Action space: target joint positions for PD controller

class LocomotionPolicy(torch.nn.Module):
    def __init__(self, obs_dim=48, act_dim=12):
        super().__init__()
        self.network = torch.nn.Sequential(
            torch.nn.Linear(obs_dim, 256),
            torch.nn.ELU(),
            torch.nn.Linear(256, 256),
            torch.nn.ELU(),
            torch.nn.Linear(256, act_dim),
        )

    def forward(self, obs):
        return self.network(obs)

# Reward function components:
# + Forward velocity tracking
# + Upright torso orientation
# - Energy consumption
# - Joint torque penalties
# - Foot slip penalty

Terrain Adaptation

Real-world robots must handle diverse terrain. Modern approaches use:

• Heightmap encoding: A terrain scan around the robot's feet is encoded as input to the policy network
• Privileged learning: Train with ground-truth terrain info in simulation, then distill to a vision-only policy for deployment
• Curriculum learning: Gradually increase terrain difficulty during training - start on flat ground, progress to stairs and rough terrain

Dynamic Movement

Beyond walking, advanced humanoid locomotion includes:

• Running: Both feet leave the ground (flight phase), requiring different balance strategies
• Stair climbing: Precise foot placement and weight shifting
• Recovery: Regaining balance after pushes or trips
• Crouching and crawling: Navigating constrained spaces