Motrixlab/docs/source/en/user_guide/demo/dm_walker.md

2D Walker Robot

The 2D Walker Robot (Walker2D) is a classic robot control task from DeepMind Control Suite. The goal is to achieve standing, walking, and running by controlling the robot's joints.

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Task Description

Walker2D is a 2D planar bipedal robot with multiple joints and actuators:

• State Space: Includes rotation angles and angular velocities of various robot parts, torso height and velocity, etc.
• Action Space: Control torques for each joint
• Reward Function: Mainly composed of maintaining standing balance and forward speed
• Termination Conditions: Robot falls or joints reach limit positions

Three Task Modes

1. dm-stander: Static standing task (move_speed = 0.0)

uv run scripts/train.py --env dm-stander

2. dm-walker: Walking task (move_speed = 1.0)

uv run scripts/train.py --env dm-walker

3. dm-runner: Running task (move_speed = 5.0)

uv run scripts/train.py --env dm-runner

Quick Start

1. Environment Preview

uv run scripts/view.py --env dm-stander
uv run scripts/view.py --env dm-walker
uv run scripts/view.py --env dm-runner

2. Start Training

uv run scripts/train.py --env dm-stander
uv run scripts/train.py --env dm-walker
uv run scripts/train.py --env dm-runner

3. View Training Progress

uv run tensorboard --logdir runs/dm-walker

4. Test Training Results

uv run scripts/play.py --env dm-stander
uv run scripts/play.py --env dm-walker
uv run scripts/play.py --env dm-runner

Reward Function Design

Walker2D's reward function consists of the following components:

Basic Standing Reward

# Height reward: keep torso at target height

# Upright reward: keep torso upright

Movement Reward (walking and running tasks)

# Speed reward: track target speed

# Total reward = standing reward * movement weight

Expected Results

1. dm-stander:

   • Torso height maintained in 1.0-1.4m range

2. dm-walker:

   • Actual walking speed close to 1.0 m/s

3. dm-runner:

   • Running speed reaches 4.0-5.0 m/s