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Prepare Tasks for GBC Training

Welcome to the exciting world of robot training with GBC! 🤖 In this comprehensive guide, we'll take you on a journey from zero to hero, teaching you how to create sophisticated robot training tasks that can learn complex behaviors through imitation learning and reinforcement learning.

Prerequisites Required

Before we dive into the adventure, please ensure you've mastered these foundational tutorials - think of them as your training wheels before the real ride begins! 🎯

Essential Reading

📚 Must-Read IsaacLab Tutorials:

🏗️ Basic Task Architecture​

Every great robot training task follows a well-organized structure - like a blueprint for success! Here's what your task directory should look like when you're done:

Task Structure
your_awesome_task/ 🎪
├── __init__.py                    # 🎫 Task registration (your entry ticket!)
├── flat_env_cfg.py               # 🏁 Flat terrain config (training wheels)
├── rough_env_cfg.py              # ⛰️  Rough terrain config (the real challenge!)
├── dagger_env_cfg.py             # 🎯 DAgger config (teacher-student magic)
├── agents/                       # 🧠 The brains of the operation
│   ├── __init__.py              # 📋 Agent registration
│   ├── rsl_rl_ppo_cfg.py        # 🎮 Standard PPO agent
└── └── rsl_rl_ref_ppo_cfg.py    # ✨ Reference-enhanced PPO agent
What Makes This Structure Special?
  • Progressive Difficulty: Start on flat ground, graduate to mountains! 🏔️
  • Multi-Modal Learning: Combine imitation learning with exploration 🎭
  • Flexible Training: From basic RL to advanced reference-guided learning 🌟

Now, let's embark on this step-by-step journey to create your very own robot training masterpiece! 🎨

🎯 Step 1: Create Your Robot Configuration File​

Time to bring your robot to life! 🤖✨

Create a new file named your_robot.py under GBC.gyms.isaaclab_45.lab_assets - this is where the magic begins! We have a treasure trove of examples already waiting for you to explore and learn from.

Critical Prerequisite

🚨 Important Prerequisite Alert! Before diving in, you'll need to convert your robot URDF to the powerful USD format. Think of this as translating your robot's blueprint into a language that Isaac Sim understands perfectly!

Need Help with URDF → USD Conversion?

🤔 Feeling lost about URDF → USD conversion? No worries! Check out this fantastic step-by-step tutorial that will walk you through the process like a friendly guide. 🗺️

🌟 Real-World Example: Unitree Robot Configuration​

Let's dive into a concrete example using Unitree robots! Here's how to create a comprehensive robot configuration that covers all the essential components:

📁 File Structure and Imports​

# Copyright header and imports
import isaaclab.sim as sim_utils
from isaaclab.actuators import ActuatorNetMLPCfg, DCMotorCfg, ImplicitActuatorCfg
from isaaclab.assets.articulation import ArticulationCfg
from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR

⚙️ 1. Actuator Configuration - The Robot's Muscles!​

Different robots need different types of "muscles" (actuators). Here are the main types you'll encounter:

DC Motor Configuration (Simple and Reliable)

🔋 Perfect for: Basic robots, educational projects, and reliable operation

DCMotorCfg(
    joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"],
    effort_limit=33.5,        # Maximum torque (N⋅m)
    saturation_effort=33.5,   # Torque saturation limit
    velocity_limit=21.0,      # Maximum joint velocity (rad/s)
    stiffness=25.0,          # Joint stiffness (N⋅m/rad)
    damping=0.5,             # Joint damping (N⋅m⋅s/rad)
    friction=0.0,            # Joint friction
)
MLP-Based Actuator (AI-Powered Motors)

🧠 Perfect for: Research robots with learned actuator dynamics

GO1_ACTUATOR_CFG = ActuatorNetMLPCfg(
    joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"],
    network_file=f"{ISAACLAB_NUCLEUS_DIR}/ActuatorNets/Unitree/unitree_go1.pt",
    pos_scale=-1.0,          # Position input scaling
    vel_scale=1.0,           # Velocity input scaling
    torque_scale=1.0,        # Torque output scaling
    input_order="pos_vel",   # Input format: position then velocity
    input_idx=[0, 1, 2],     # Input indices to use
    effort_limit=23.7,       # Maximum effort from spec sheet
    velocity_limit=30.0,     # Maximum velocity from spec sheet
    saturation_effort=23.7,  # Saturation limit
)
Implicit Actuator (For Advanced Control)

⚡ Perfect for: High-performance robots requiring precise control

ImplicitActuatorCfg(
    joint_names_expr=[".*_hip_yaw", ".*_hip_roll", ".*_hip_pitch"],
    effort_limit=300,        # Maximum effort
    velocity_limit=100.0,    # Maximum velocity
    stiffness={              # Different stiffness for different joints
        ".*_hip_yaw": 150.0,
        ".*_hip_roll": 150.0,
        ".*_hip_pitch": 200.0,
    },
    damping={               # Different damping for different joints
        ".*_hip_yaw": 5.0,
        ".*_hip_roll": 5.0,
        ".*_hip_pitch": 5.0,
    },
)

🏗️ 2. Main Robot Configuration - Putting It All Together​

Here's a complete example using the Unitree A1 quadruped:

UNITREE_A1_CFG = ArticulationCfg(
    # 🎬 Spawn Configuration - How your robot appears in the world
    spawn=sim_utils.UsdFileCfg(
        usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/A1/a1.usd",  # 👈 REPLACE THIS!
        activate_contact_sensors=True,  # Enable contact detection
        
        # 🏋️ Physical Properties - How your robot behaves physically
        rigid_props=sim_utils.RigidBodyPropertiesCfg(
            disable_gravity=False,           # Gravity affects the robot
            retain_accelerations=False,      # Don't store acceleration history
            linear_damping=0.0,             # No linear damping
            angular_damping=0.0,            # No angular damping
            max_linear_velocity=1000.0,     # Maximum linear speed (m/s)
            max_angular_velocity=1000.0,    # Maximum angular speed (rad/s)
            max_depenetration_velocity=1.0, # Collision resolution speed
        ),
        
        # 🔧 Articulation Properties - Joint solver settings
        articulation_props=sim_utils.ArticulationRootPropertiesCfg(
            enabled_self_collisions=True,           # Robot parts can collide with each other
            solver_position_iteration_count=4,      # Position accuracy vs speed
            solver_velocity_iteration_count=0,      # Velocity accuracy vs speed
        ),
    ),
    
    # 🎯 Initial State - Where your robot starts
    init_state=ArticulationCfg.InitialStateCfg(
        pos=(0.0, 0.0, 0.42),           # Starting position (x, y, z) in meters
        joint_pos={                     # Starting joint angles in radians
            ".*L_hip_joint": 0.1,       # Left hip joints
            ".*R_hip_joint": -0.1,      # Right hip joints (mirrored)
            "F[L,R]_thigh_joint": 0.8,  # Front thigh joints
            "R[L,R]_thigh_joint": 1.0,  # Rear thigh joints
            ".*_calf_joint": -1.5,      # All calf joints
        },
        joint_vel={".*": 0.0},          # Start with zero velocity
    ),
    
    # 🛡️ Safety Settings
    soft_joint_pos_limit_factor=0.9,    # Use 90% of joint limits for safety
    
    # 💪 Actuators - The robot's muscle system
    actuators={
        "base_legs": DCMotorCfg(
            joint_names_expr=[".*_hip_joint", ".*_thigh_joint", ".*_calf_joint"],
            effort_limit=33.5,
            saturation_effort=33.5,
            velocity_limit=21.0,
            stiffness=25.0,
            damping=0.5,
            friction=0.0,
        ),
    },
)

🔄 3. For Your Custom Robot - Essential Modifications​

When adapting this for your own robot, you'll need to modify these key areas:

Critical Path Configuration

📂 USD Path (MOST IMPORTANT!)

# Replace this line:
usd_path=f"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/A1/a1.usd"

# With your own USD file path:
usd_path="/path/to/your/robot.usd"
# or if it's in your project:
usd_path=f"{PROJECT_ROOT_DIR}/urdf_models/your_robot/your_robot.usd"
Joint Configuration

🎯 Initial Joint Positions

# Study your robot's URDF and set appropriate starting positions
joint_pos={
    "joint_name_1": 0.0,      # Replace with your actual joint names
    "joint_name_2": 1.57,     # Use realistic starting angles
    ".*_pattern_.*": -0.5,    # Use regex patterns for similar joints
}

🦵 Joint Name Patterns

# Update actuator configurations with your robot's joint names
joint_names_expr=["your_hip_.*", "your_knee_.*", "your_ankle_.*"]

🎨 4. Advanced Configurations - Multiple Robot Variants​

Robot Variants

You can create multiple variants of the same robot for different use cases:

# 🏃 Minimal Configuration (Faster Simulation)
YOUR_ROBOT_MINIMAL_CFG = YOUR_ROBOT_CFG.copy()
YOUR_ROBOT_MINIMAL_CFG.spawn.usd_path = "/path/to/your_robot_minimal.usd"
YOUR_ROBOT_MINIMAL_CFG.spawn.articulation_props.enabled_self_collisions = False

# 🎯 High-Precision Configuration (Research Quality)
YOUR_ROBOT_PRECISE_CFG = YOUR_ROBOT_CFG.copy()
YOUR_ROBOT_PRECISE_CFG.spawn.articulation_props.solver_position_iteration_count = 8
YOUR_ROBOT_PRECISE_CFG.spawn.articulation_props.solver_velocity_iteration_count = 4
Pro Tips for Success
  1. 🔍 Joint Name Investigation: Use your robot's URDF to understand the exact joint names
  2. ⚖️ Physical Parameters: Check your robot's spec sheet for accurate motor limits
  3. 🎯 Starting Pose: Set a stable, realistic starting configuration
  4. 🔄 Iterative Testing: Start simple, then add complexity gradually
  5. 📊 Performance vs Accuracy: Balance solver iterations based on your needs

🎪 What's Next?​

Once you've created your robot configuration file, you'll use it in your environment configurations. The robot configuration acts as the foundation - like choosing the perfect actor for your robot training drama! 🎭

Checklist Before Moving On

✅ Completion Verification:

  • USD file is properly converted and accessible
  • Joint names match your robot's URDF
  • Physical parameters are realistic
  • Initial pose is stable
  • Actuator limits match specification sheets

Ready to move on to creating the environment where your robot will learn to shine? Let's go! 🚀

🏔️ Step 2: Create Your Environment Configuration (Rough Env)​

Welcome to the heart of robot training - the environment configuration! 🌍 This is where we define the challenging world your robot will explore, complete with rewards, observations, and all the complex interactions that will shape its learning journey. We'll use the Turin humanoid robot as our guiding example!

🎯 File Overview: rough_env_cfg.py​

This file extends IsaacLab's locomotion environment with GBC's advanced features. Think of it as creating a sophisticated training gymnasium with multiple difficulty levels and specialized equipment! 🏋️‍♂️

📦 Essential Imports and Setup​

Required Imports
# Core IsaacLab components
from isaaclab.managers import RewardTermCfg as RewTerm
from isaaclab.managers import SceneEntityCfg
from isaaclab.managers import TerminationTermCfg as DoneTerm
from isaaclab.utils import configclass

# Base environment and configurations
from isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import (
    LocomotionVelocityRoughEnvCfg, ObservationsCfg, RewardsCfg, EventCfg
)

# GBC specialized components
from GBC.gyms.isaaclab_45.managers.ref_obs_term_cfg import ReferenceObservationTermCfg as RefObsTerm
from GBC.gyms.isaaclab_45.managers.physics_modifier_cfg import PhysicsModifierTermCfg as PhxModTerm

# Your robot configuration from Step 1! 🎉
from GBC.gyms.isaaclab_45.lab_assets.turin_v3 import TURIN_V3_CFG

🔧 Core Components to Configure​

1. 🔄 Symmetry System - Teaching Balance​

Understanding Symmetry

Create functions that understand your robot's left-right symmetry:

def get_flipper():
    """Get a flipper instance for left-right symmetry operations."""
    return YourRobotNameFlipLeftRight()

def get_observation_symmetry(env, observations, history_length=4):
    """Define how observations transform under left-right flipping."""
    # Maps observations to their symmetric counterparts
    # Essential for data augmentation and stable learning!

🎨 Key Elements:

  • Joint Symmetry: Map left joints to right joints and vice versa
  • Velocity Symmetry: Handle directional changes in velocities
  • Phase Symmetry: Swap left/right foot phase information

2. 👁️ AMP Observation System - What Matters for Imitation​

AMP Observation Processing

Define which observations are crucial for imitation learning:

def get_amp_observations(observations, env, history_length=4):
    """Extract key observations for AMP (Adversarial Motion Prior)."""
    # Select the most important features for motion discrimination
    return amp_observations

def get_amp_ref_observations(ref_observations, env):
    """Process reference observations for AMP training."""
    # Align reference data with policy observations
    return processed_ref_obs, masks

🎯 Typical AMP Components:

  • Base linear/angular velocities
  • Joint positions and velocities
  • Gravity direction
  • Contact phase information

3. 🏃‍♂️ Reward System Architecture​

Organize rewards into logical categories for different training aspects:

🎮 Base Locomotion Rewards (YourRobotNameRewards)

@configclass
class YourRobotNameRewards(RewardsCfg):
    # Core locomotion behaviors
    track_lin_vel_xy_exp = RewTerm(...)  # Follow velocity commands
    track_ang_vel_z_exp = RewTerm(...)   # Follow rotation commands
    feet_air_time = RewTerm(...)         # Natural gait patterns
    feet_slide = RewTerm(...)            # Prevent foot sliding
    # ... and many more locomotion-specific rewards

🎯 Reference Tracking Rewards (YourRobotNameRefTrackActionRewards)

@configclass
class YourRobotNameRefTrackActionRewards(RewardsCfg):
    # Imitation learning specific rewards
    tracking_target_actions_lower_body = RewTerm(...)  # Match reference poses
    tracking_target_actions_hip = RewTerm(...)         # Hip joint accuracy
    tracking_target_actions_ankle = RewTerm(...)       # Ankle precision
    # Curriculum learning with adaptive standards!

🎭 Pose and Motion Rewards (YourRobotNameRefTrackPoseRewards & YourRobotNameRefOtherRewards)

@configclass  
class YourRobotNameRefTrackPoseRewards(RewardsCfg):
    # Advanced pose matching and motion quality rewards
    
@configclass
class YourRobotNameRefOtherRewards(RewardsCfg):
    # Auxiliary rewards for stability and naturalness

4. ⚡ Physics Modifiers - Curriculum Learning Tools​

Add intelligent training assistance that adapts over time:

@configclass
class PhysicsModifiersCfg:
    external_z_force_base = PhxModTerm(
        func=external_z_force_base,
        params={
            "max_force": 1600.0,              # Baby walker assistance
            "apply_offset_range": 0.2,        # When to apply help
            "apply_force_duration_ratio": 0.8, # How long to help
            # Adaptive parameters that change based on performance!
        },
        description="Adaptive external z-force for curriculum learning"
    )

5. 🎬 Event System - Dynamic Training Scenarios​

Configure how episodes start and environmental variations:

@configclass
class YourRobotNameEventCfg(EventCfg):
    reset_start_time = EventTerm(
        func=randomize_initial_start_time,
        mode="reset",
        params={"sample_episode_ratio": 1.0}
    )
    # Add terrain randomization, robot pose variations, etc.

6. 👀 Observation Configurations​

Define what your robot can "see" and remember:

@configclass
class YourRobotNameObservationsCfg(ObservationsCfg):
    # Standard locomotion observations with history
    # Joint positions, velocities, contact information, etc.

@configclass  
class YourRobotNameRefObservationCfg(RefObsCfg):
    # Reference observation system for imitation learning
    # Links to reference motion data and processing

7. 🏞️ Observation Working Mode - "recurrent", "recurrent_strict", or "singular"​

While creating your observation configurations, you can choose how the observations are processed:

@configclass
class YourRobotNameObservationWorkingModeCfg(ObservationsCfg):
    ...

    working_mode: Literal["recurrent", "recurrent_strict", "singular"] = "recurrent"
How these modes work

These three modes are compatible with GBC.utils.buffer.ref_buffer to load reference data in three different ways:

  • "recurrent": The data with cyclic_subseq will be loaded in form of "start -> cyclic_begin -> cyclic_end -> cyclic_begin" mode and some of the features will be calculated accordingly. This is similar to the cycle of music. However, for data without cyclic_subseq, it will be loaded in a "start -> end" mode. After played, the reference buffer will be disabled and the agent functions purely according to random commands.

  • "recurrent_strict": Only data with cyclic_subseq will be loaded to train the agent. Other environments will be masked out by a zero tensor. This is similar to the "recurrent" mode, but it will not load any data without cyclic_subseq. This is useful for training the agent with only cyclic data.

  • "singular": The data will be loaded in a "start -> end" mode, and the reference buffer will be disabled after played. This is similar to the "recurrent" mode, but it will not load any cyclic data. This is useful for training the agent with only singular data.

🏗️ Environment Variants - Progressive Difficulty​

Create multiple environment configurations for different training stages:

🎯 Main Training Environment​

@configclass
class YourRobotNameRoughEnvCfg(LocomotionVelocityRoughEnvCfg):
    def __post_init__(self):
        # Link your robot configuration from Step 1!
        self.scene.robot = TURIN_V3_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
        # Configure terrain, rewards, observations, etc.

🎮 Inference Environment​

@configclass
class YourRobotNameRoughEnvCfg_PLAY(YourRobotNameRoughEnvCfg):
    def __post_init__(self):
        super().__post_init__()
        # Optimized for evaluation and demonstration
        self.scene.num_envs = 64
        self.episode_length_s = 20.0

✨ Reference-Enhanced Environment​

@configclass
class YourRobotNameRoughRefEnvCfg(YourRobotNameRoughEnvCfg):
    # Combines standard RL with imitation learning
    ref_observation = YourRobotNameRefObservationCfg()
    rewards = YourRobotNameRefRewards()  # Multi-component reward system

🎨 Key Configuration Principles​

Design Philosophy

🔄 Modular Design​

  • Separate Concerns: Different reward classes for different aspects
  • Inheritance: Build complex configurations from simple components
  • Reusability: Share common elements across variants

📈 Progressive Learning​

  • Curriculum Integration: Physics modifiers that adapt to performance
  • Multi-Stage Rewards: From basic locomotion to advanced imitation
  • Difficulty Scaling: Easy → Medium → Hard environment variants

🎯 Reference Integration​

  • Dual Observation: Standard + reference observation systems
  • Flexible Modes: Pure RL, pure IL, or hybrid training
  • Data Compatibility: Seamless integration with motion capture data
Essential Customizations for Your Robot

When adapting this for your robot:

  1. 🤖 Robot Reference: Replace TURIN_V3_CFG with your robot config from Step 1
  2. 🦵 Joint Names: Update all joint name patterns to match your robot
  3. 👣 Contact Bodies: Specify your robot's foot/contact link names
  4. ⚖️ Reward Weights: Tune reward weights based on your robot's capabilities
  5. 🔄 Symmetry Mapping: Create your robot's left-right joint mapping
Completion Checklist

Before moving to the next step, ensure you have:

  • Robot configuration properly imported and referenced
  • Symmetry functions defined for your robot's joint structure
  • AMP observation extraction matching your robot's key features
  • Reward system covering locomotion, imitation, and stability
  • Physics modifiers configured for curriculum learning
  • Multiple environment variants (training, play, reference)
  • Observation configurations for both standard and reference modes

🎉 Congratulations! You've just created a sophisticated training environment that can teach your robot everything from basic walking to advanced imitation skills! Next up: organizing different training modes for progressive learning! 🚀

🏋️‍♂️ Step 3: Create Your DAgger Environment (Optional)​

Time for the specialized DAgger training setup! 🎯 This is a simplified, focused environment designed to teach your PPO agent the fundamentals of reference tracking before venturing into the complex real world.

🎪 What Makes DAgger Special?​

DAgger Training Philosophy

The DAgger (Dataset Aggregation) environment is like a "practice gym" - it strips away complexity to focus on one crucial skill: learning to follow reference motions perfectly. Think of it as teaching your robot to be a great dancer before asking it to dance on a tightrope! 💃

Key Characteristics

🎯 DAgger Features:

  • 🔒 Fixed Base: Robot base is locked in place to focus purely on joint control
  • 📊 Contact Simulation: Artificial contact feedback without terrain complexity
  • 🎮 Simplified Rewards: Only essential tracking rewards, no locomotion distractions
  • 🔄 Direct Observation Sync: All observations inherited from your rough environment

🏗️ DAgger Configuration Structure​

@configclass  
class YourRobotNameDaggerRewards(RewardsCfg):
    """Simplified reward system focusing only on reference tracking."""
    
    # Essential safety and basic behavior
    termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0)
    feet_air_time = RewTerm(...)        # Basic gait timing
    dof_pos_limits = RewTerm(...)       # Joint safety
    
    # Core tracking rewards only - no locomotion complexity!
    # tracking_target_actions_* terms for precise motion matching

@configclass
class YourRobotNameDaggerRefRewards(YourRobotNameDaggerRewards):
    """Extended tracking rewards with curriculum learning."""
    
    # Separate tracking for different body parts
    tracking_target_actions_lower_body_left = RewTerm(...)   # Left leg precision
    tracking_target_actions_lower_body_right = RewTerm(...) # Right leg precision  
    tracking_target_actions_upper_body_left = RewTerm(...)  # Left arm coordination
    tracking_target_actions_upper_body_right = RewTerm(...) # Right arm coordination
    tracking_target_actions_torso = RewTerm(...)            # Core stability
    
    # All with adaptive curriculum learning! 📈

@configclass
class YourRobotNameDaggerRefEnvCfg(LocomotionVelocityRoughEnvCfg):
    """The complete DAgger training environment."""
    
    def __post_init__(self):
        super().__post_init__()
        
        # 🔒 Lock the base for focused joint training
        self.scene.robot.spawn.articulation_props.fix_root_link = True
        
        # 🏁 Simplified terrain - flat plane only
        self.scene.terrain.terrain_type = "plane"
        self.scene.height_scanner = None
        
        # 🎯 Fixed movement commands for consistency
        self.commands.base_velocity.ranges.lin_vel_x = (0.5, 0.5)
        self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
        self.commands.base_velocity.ranges.ang_vel_z = (0.0, 0.0)
        
        # 🧹 Remove environmental randomization
        self.events.push_robot = None
        self.events.base_external_force_torque.params["asset_cfg"].body_names = [".*torso_link"]

🎓 The DAgger Training Philosophy​

Progressive Training Strategy
  1. 🎯 Step 1: Learn perfect reference tracking in controlled conditions
  2. 🚀 Step 2: Transfer learned skills to complex rough environments
  3. 🌍 Step 3: Deploy to real-world scenarios with confidence!

This two-stage approach ensures your robot masters the fundamentals before facing real-world challenges - like learning to walk before learning to run! 🏃‍♂️

Observation Compatibility

🔄 Observation Inheritance: All observation configurations are directly synced from your rough environment configuration, ensuring perfect compatibility and smooth knowledge transfer between training stages.

Quick Setup Checklist
  • DAgger environment inherits from your rough environment
  • Base is fixed (fix_root_link = True)
  • Only tracking rewards enabled (no locomotion rewards)
  • Flat terrain with no environmental randomization
  • Consistent velocity commands for stable training

🎉 Ready for the next step? With both rough and DAgger environments configured, you're ready to create the intelligent agents that will bring your robot to life! 🤖✨

🏁 Step 4: Create Flat Environment Configuration​

Time to simplify things! 🎯 The flat environment is your robot's training wheels - it takes your complex rough environment and strips away the challenging terrain features for easier, more stable learning.

🎪 What's Different About Flat Environment?​

Think of this as moving from a rocky mountain trail to a smooth gymnasium floor! 🏟️ Perfect for when you want your robot to focus purely on motion patterns without terrain distractions.

🔧 Simple Modifications:

@configclass
class YourRobotNameFlatEnvCfg(YourRobotNameRoughEnvCfg):
    def __post_init__(self):
        super().__post_init__()
        
        # 🏁 Simplified terrain - no more mountains!
        self.scene.terrain.terrain_type = "plane"
        self.scene.terrain.terrain_generator = None
        
        # 👁️ No height scanning needed on flat ground
        self.scene.height_scanner = None
        self.observations.policy.height_scan = None
        
        # 📚 No terrain curriculum progression
        self.curriculum.terrain_levels = None
        
        # 🦶 Adjust gait parameters for flat surface
        self.rewards.feet_air_time.weight = 1.0
        self.rewards.feet_air_time.params["threshold"] = 0.6

🎮 Environment Variants:

  • YourRobotNameFlatEnvCfg: Basic flat training environment
  • YourRobotNameFlatEnvCfg_PLAY: Optimized for evaluation and demos
  • YourRobotNameFlatRefEnvCfg: Flat environment with reference tracking
  • YourRobotNameFlatRefEnvCfg_PLAY: Reference-enabled evaluation setup

🎯 Key Simplifications​

Simplified Environment Features
  1. 🏔️ → 🏁 Terrain: Rocky terrain → Smooth plane
  2. 👁️ Sensors: Height scanner removed (no terrain to scan!)
  3. 📈 Curriculum: No terrain difficulty progression
  4. 🎛️ Parameters: Adjusted for flat surface conditions
Quick Implementation

Simply inherit from your rough environment and override the terrain settings - it's that easy! All your complex reward systems, observations, and robot configurations remain intact.

💡 Perfect For:

  • Initial robot training and testing
  • Algorithm debugging and development
  • Baseline performance evaluation
  • Demonstration and visualization

Ready to create the intelligent agents that will control your robot? Let's dive into the task registration next! 🧠✨

📝 Step 5: Task Registration​

Time to make your environments discoverable! 🎫 This is where we register all your carefully crafted environments with the Gym registry, following specific naming conventions and pointing to the right entry points.

🏷️ Naming Convention Rules​

Critical Naming Pattern

All task IDs follow this strict pattern:

Isaac-Velocity-{TASK_TYPE}-{YOUR_ROBOT_NAME}-{REF or NOT}-{Version ID}
Component Breakdown

🎯 Task Type Examples:

  • Rough: Complex terrain environments
  • Flat: Simplified flat terrain
  • Dagger: DAgger training environments

🤖 Robot Name Examples:

  • YourRobotName: Unitree YourRobotName humanoid
  • A1: Unitree A1 quadruped
  • YourRobot: Your custom robot name

✨ Reference Indicator:

  • Reference: For imitation learning tasks
  • (omitted): For standard RL tasks

🎮 Special Modifiers:

  • Play: Added before version for evaluation environments
  • v0, v1, etc.: Version identifier
Real Examples from YourRobotName Robot
# 🏔️ Complex terrain training
"Isaac-Velocity-Rough-YourRobotName-v0"                    # Standard RL
"Isaac-Velocity-Rough-YourRobotName-Reference-v0"          # Reference RL
"Isaac-Velocity-Rough-YourRobotName-Play-v0"               # Evaluation

# 🏁 Flat terrain training  
"Isaac-Velocity-Flat-YourRobotName-v0"                     # Standard RL
"Isaac-Velocity-Flat-YourRobotName-Reference-v0"           # Reference RL
"Isaac-Velocity-Flat-YourRobotName-Play-v0"                # Evaluation

# 🎯 DAgger specialized training
"Isaac-Velocity-Dagger-YourRobotName-v0"                   # DAgger training
"Isaac-Velocity-Dagger-YourRobotName-Play-v0"              # DAgger evaluation

🔌 Entry Point Configuration​

The most critical aspect of registration is choosing the correct entry points:

🎮 Standard RL Environments​

gym.register(
    id="Isaac-Velocity-Flat-YourRobotName-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",  # 👈 Standard IsaacLab
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": flat_env_cfg.YourRobotNameFlatEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:YourRobotNameFlatPPORunnerCfg",
    },
)

✨ Reference RL Environments (Imitation Learning)​

gym.register(
    id="Isaac-Velocity-Flat-YourRobotName-Reference-v0",
    entry_point="GBC.gyms.isaaclab_45.envs:ManagerBasedRefRLEnv",  # 👈 GBC Extension!
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": flat_env_cfg.YourRobotNameFlatRefEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ref_ppo_cfg:YourRobotNameFlatRefPPORunnerCfg",
    },
)

🎮 Play/Evaluation Environments​

gym.register(
    id="Isaac-Velocity-Flat-YourRobotName-Play-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": flat_env_cfg.YourRobotNameFlatEnvCfg_PLAY,  # 👈 _PLAY variant
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:YourRobotNameFlatPPORunnerCfg",
    },
)
Critical Entry Point Rules
  1. 📚 Standard RL: Use isaaclab.envs:ManagerBasedRLEnv
  2. ✨ Reference RL: MUST use GBC.gyms.isaaclab_45.envs:ManagerBasedRefRLEnv
  3. 🎯 Environment Config: Points to your environment configuration classes
  4. 🧠 Agent Config: Links to appropriate PPO runner configurations

🏗️ Complete Registration Example​

Here's how to register a full suite of environments for your robot:

import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg, dagger_env_cfg

# 🏔️ Rough Terrain Environments
gym.register(
    id="Isaac-Velocity-Rough-YourRobot-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": rough_env_cfg.YourRobotRoughEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:YourRobotRoughPPORunnerCfg",
    },
)

gym.register(
    id="Isaac-Velocity-Rough-YourRobot-Reference-v0",
    entry_point="GBC.gyms.isaaclab_45.envs:ManagerBasedRefRLEnv",  # 🎯 GBC Entry Point!
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": rough_env_cfg.YourRobotRoughRefEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ref_ppo_cfg:YourRobotRoughRefPPORunnerCfg",
    },
)

# 🏁 Flat Terrain Environments
gym.register(
    id="Isaac-Velocity-Flat-YourRobot-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": flat_env_cfg.YourRobotFlatEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:YourRobotFlatPPORunnerCfg",
    },
)


# 🎯 DAgger Environment
gym.register(
    id="Isaac-Velocity-Dagger-YourRobot-v0",
    entry_point="GBC.gyms.isaaclab_45.envs:ManagerBasedRefRLEnv",  # 🎯 Reference RL Required!
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": dagger_env_cfg.YourRobotDaggerRefEnvCfg,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ref_ppo_cfg:YourRobotTrainDAggerPPORunnerCfg",
    },
)

# 🎮 Play Environments (Add "Play" before version)
gym.register(
    id="Isaac-Velocity-Flat-YourRobot-Play-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": flat_env_cfg.YourRobotFlatEnvCfg_PLAY,
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:YourRobotFlatPPORunnerCfg",
    },
)

🎯 Key Configuration Parameters​

Configuration Components

📁 Environment Configuration (env_cfg_entry_point)

  • Points to your environment class (e.g., YourRobotNameFlatEnvCfg)
  • Defines the simulation world, rewards, observations
  • Different configs for training vs. play variants

🧠 Agent Configuration (rsl_rl_cfg_entry_point)

  • Points to PPO runner configuration
  • Defines neural network architecture, training hyperparameters
  • Separate configs for standard vs. reference RL

🔧 Additional Options (skrl_cfg_entry_point)

  • Alternative training backend configuration
  • Optional for advanced users
Registration Checklist

Before testing your registered environments:

  • All environment configurations imported correctly
  • Naming convention follows the Isaac-Velocity pattern
  • Reference tasks use GBC entry point
  • Standard tasks use IsaacLab entry point
  • Agent configurations match environment types
  • Play variants use _PLAY environment configs

🎉 Success! Your environments are now officially registered and ready to be discovered by training scripts! Next up: creating the intelligent agents that will bring your robot to life! 🤖🚀

🚀 Step 6: Set Up RSL_RL Training Parameters​

Time to create the brains of your robot! 🧠 This step involves configuring two types of training agents: standard PPO for basic reinforcement learning and reference PPO for advanced imitation learning with all the bells and whistles.

🎯 Agent Configuration Overview​

Agent Architecture

You'll need to create two agent configuration files in your agents/ folder:

agents/ 🧠
├── __init__.py                    # Agent registration
├── rsl_rl_ppo_cfg.py             # 🎮 Standard PPO (Basic RL)
└── rsl_rl_ref_ppo_cfg.py         # ✨ Reference PPO (Advanced IL+RL)

🎮 Standard PPO Configuration (rsl_rl_ppo_cfg.py)​

Basic RL Agent

This is your basic RL agent - straightforward and focused on pure reinforcement learning without imitation learning complexity.

📦 Essential Imports​

from isaaclab.utils import configclass
from GBC.gyms.isaaclab_45.lab_tasks.utils.wrappers.rsl_rl import (
    RslRlRefPpoActorCriticCfg,
    RslRlRefPpoAlgorithmCfg,
    RslRlRefOnPolicyRunnerCfg,
)
from typing import Literal
from GBC.gyms.isaaclab_45.lab_tasks.your_robot.rough_env_cfg import GLOBAL_HISTORY_LENGTH

🏗️ Basic PPO Runner Configuration​

@configclass
class YourRobotNameRoughPPORunnerCfg(RslRlRefOnPolicyRunnerCfg):
    # 🎲 Training Parameters
    num_steps_per_env = 24              # Steps per environment per iteration
    max_iterations = 3000               # Total training iterations
    save_interval = 50                  # Save model every N iterations
    experiment_name = "your_robot_rough" # Experiment identifier
    empirical_normalization = False     # Use empirical observation normalization
    
    # 🧠 Policy Network Configuration
    policy = RslRlRefPpoActorCriticCfg(
        class_name="ActorCriticMMTransformerV2",  # Network architecture
        max_len=8,                        # Sequence length for transformer
        dim_model=256,                    # Model dimension
        num_layers=2,                     # Number of transformer layers
        num_heads=8,                      # Number of attention heads
        init_noise_std=1.0,              # Initial action noise
        load_dagger=False,                # No DAgger pre-training for basic RL
        apply_mlp_residual=False,         # No residual connections
        history_length=GLOBAL_HISTORY_LENGTH, # Observation history length
        
        # 🔗 Observation Concatenation (Critical! Set this group according to your own observation terms! Here is just an example.)
        concatenate_term_names={
            "policy": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "projected_gravity"]
            ],
            "critic": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "projected_gravity"]
            ],
        },
        concatenate_ref_term_names={
            "policy": [],
            "critic": [],
        },
    )
    
    # 🎯 PPO Algorithm Configuration
    algorithm = RslRlRefPpoAlgorithmCfg(
        class_name="MMPPO",               # Multi-modal PPO algorithm
        value_loss_coef=1.0,             # Value function loss weight
        use_clipped_value_loss=True,     # Use clipped value loss
        clip_param=0.4,                  # PPO clipping parameter
        entropy_coef=1e-2,               # Entropy bonus coefficient
        num_learning_epochs=4,           # Epochs per iteration
        num_mini_batches=8,              # Mini-batches per epoch
        learning_rate=1.0e-4,            # Learning rate
        schedule="adaptive",             # Learning rate schedule
        normalize_advantage_per_mini_batch=True, # Advantage normalization
        gamma=0.99,                      # Discount factor
        lam=0.95,                        # GAE lambda
        desired_kl=0.075,               # Target KL divergence
        max_grad_norm=0.5,              # Gradient clipping
        
        # 🚫 No advanced features for basic PPO
        rnd_cfg=None,                    # No curiosity-driven exploration
        symmetry_cfg=None,               # No symmetry augmentation
        amp_cfg=None,                    # No adversarial motion prior
    )
    
    # 📊 Logging Configuration
    run_name: str = "your_robot_basic"
    logger: Literal["tensorboard", "neptune", "wandb"] = "tensorboard"
    resume: bool = False

# 🏁 Flat Terrain Variant
@configclass
class YourRobotNameFlatPPORunnerCfg(YourRobotNameRoughPPORunnerCfg):
    def __post_init__(self):
        super().__post_init__()
        self.max_iterations = 1000
        self.experiment_name = "your_robot_flat"

✨ Reference PPO Configuration (rsl_rl_ref_ppo_cfg.py)​

This is where the magic happens! 🎭 Advanced imitation learning with symmetry, AMP, DAgger, and more sophisticated features.

📦 Advanced Imports​

from typing import Literal
from isaaclab.utils import configclass
from GBC.gyms.isaaclab_45.lab_tasks.utils.wrappers.rsl_rl import (
    RslRlRefPpoActorCriticCfg,
    RslRlRefPpoAlgorithmCfg,
    RslRlRefOnPolicyRunnerCfg,
    RslRlPpoAmpCfg,
    RslRlRefPpoAmpNetCfg,
)
import torch
from GBC.gyms.isaaclab_45.lab_tasks.your_robot.rough_env_cfg import (
    get_observation_symmetry, get_amp_ref_observations, get_amp_observations, 
    GLOBAL_HISTORY_LENGTH, flipper
)
from GBC.gyms.isaaclab_45.lab_tasks.mdp import get_ref_observation_symmetry, actions_symmetry
import gym

🔄 Data Augmentation Function​

def data_augmentation_func(
        obs: torch.Tensor | None = None,
        ref_obs: torch.Tensor | None = None,
        actions: torch.Tensor | None = None,
        env: gym.Env | None = None,
        obs_type: Literal["policy", "state"] = "policy",
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Advanced data augmentation with left-right symmetry."""
    assert obs_type == "policy", "Only policy mode is supported for now"
    
    sym_obs = None
    sym_ref_obs = None
    sym_actions = None
    
    if obs is not None:
        sym_obs = get_observation_symmetry(env, obs)
    if ref_obs is not None:
        sym_ref_obs = get_ref_observation_symmetry(env, ref_obs)
    if actions is not None:
        sym_actions = actions_symmetry(env, actions, flipper=flipper)
        
    return sym_obs, sym_ref_obs, sym_actions

🔄 Symmetry Configuration​

symmetry_cfg = {
    "data_augmentation_func": data_augmentation_func,
    "use_data_augmentation": False,     # Enable/disable data augmentation
    "use_mirror_loss": True,            # Use mirror loss for symmetry
    "mirror_loss_coeff": 1.0,          # Mirror loss coefficient
}

🎭 AMP Network Configuration​

amp_net_cfg = RslRlRefPpoAmpNetCfg(
    backbone_input_dim=76,              # AMP observation dimension (adjust for your robot!)
    backbone_output_dim=128,            # Network output dimension
    backbone="mlp",                     # Network type
    activation="relu",                  # Activation function
    out_activation="sigmoid",           # Output activation
    net_kwargs={
        "hidden_dims": [512, 256],      # Hidden layer dimensions
    }
)

amp_cfg = RslRlPpoAmpCfg(
    net_cfg=amp_net_cfg,
    learning_rate=5e-4,                 # AMP discriminator learning rate
    amp_obs_extractor=get_amp_observations,     # Policy observation extractor
    amp_ref_obs_extractor=get_amp_ref_observations, # Reference observation extractor
    amp_reward_scale=0.8,               # AMP reward scaling
    epsilon=1e-4,                       # Numerical stability
    gradient_penalty_coeff=10.0,        # Gradient penalty for discriminator
    amp_update_interval=40,             # Update frequency
    amp_pretrain_steps=1000,            # Pre-training steps
)

🏆 Complete Reference PPO Runner​

@configclass
class YourRobotNameRoughRefPPORunnerCfg(RslRlRefOnPolicyRunnerCfg):
    seed = 42
    num_steps_per_env = 24
    max_iterations = 15000              # More iterations for complex learning
    save_interval = 200
    experiment_name = "your_robot_rough"
    empirical_normalization = True      # Important for reference learning!
    
    policy = RslRlRefPpoActorCriticCfg(
        class_name="ActorCriticMMTransformerV2",
        max_len=8,
        dim_model=256,
        num_layers=2,
        num_heads=8,
        init_noise_std=1.0,
        load_dagger=False,              # Set to True if you have DAgger checkpoint
        # load_dagger_path="/path/to/dagger/model.pt",  # Uncomment and set path
        apply_mlp_residual=False,
        history_length=GLOBAL_HISTORY_LENGTH,
        
        # 🔗 Enhanced Observation Concatenation
        concatenate_term_names={
            "policy": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "projected_gravity"]
            ],
            "critic": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "projected_gravity"]
            ],
        },
        concatenate_ref_term_names={
            "policy": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "target_projected_gravity"]
            ],
            "critic": [
                ["lft_sin_phase", "lft_cos_phase", "rht_sin_phase", "rht_cos_phase"], 
                ["base_lin_vel", "base_ang_vel", "target_projected_gravity"]
            ],
        },
    )
    
    algorithm = RslRlRefPpoAlgorithmCfg(
        class_name="MMPPO",
        value_loss_coef=1.0,
        use_clipped_value_loss=True,
        clip_param=0.4,
        entropy_coef=1e-2,
        num_learning_epochs=4,
        num_mini_batches=8,
        learning_rate=1.0e-4,
        schedule="adaptive",
        normalize_advantage_per_mini_batch=True,
        gamma=0.99,
        lam=0.95,
        desired_kl=0.075,
        max_grad_norm=0.5,
        
        # 🎯 DAgger Configuration (Teacher-Student Learning)
        teacher_coef_range=(0.2, 0.8),           # Teacher coefficient range
        teacher_coef_decay=0.8,                  # Decay rate
        teacher_coef_decay_interval=100,         # Decay interval
        teacher_loss_coef_range=(0.0001, 0.0025), # Teacher loss coefficient range
        teacher_loss_coef_decay=0.9995,          # Teacher loss decay
        teacher_loss_coef_decay_interval=100,    # Teacher loss decay interval
        teacher_lr=5e-4,                         # Teacher learning rate
        teacher_update_interval=10,              # Teacher update frequency
        teacher_only_interval=0,                 # Teacher-only training steps
        teacher_supervising_intervals=40000,     # Supervision duration
        teacher_updating_intervals=24000,        # Teacher update duration
        teacher_coef_mode="original_kl",         # Teacher coefficient mode
        
        # 🚀 Advanced Features
        rnd_cfg=None,                            # Random Network Distillation (optional)
        symmetry_cfg=symmetry_cfg,               # Symmetry augmentation
        amp_cfg=amp_cfg,                         # Adversarial Motion Prior
    )
    
    run_name: str = "your_robot_imitation"
    logger: Literal["tensorboard", "neptune", "wandb"] = "tensorboard"
    resume: bool = False

# 🏁 Environment-Specific Variants
@configclass
class YourRobotNameFlatRefPPORunnerCfg(YourRobotNameRoughRefPPORunnerCfg):
    def __post_init__(self):
        super().__post_init__()
        self.max_iterations = 150000
        self.experiment_name = "your_robot_flat"

@configclass
class YourRobotNameFlatRefNoDAggerPPORunnerCfg(YourRobotNameRoughRefPPORunnerCfg):
    def __post_init__(self):
        super().__post_init__()
        self.max_iterations = 150000
        self.experiment_name = "your_robot_flat_no_dagger"
        self.policy.load_dagger = False      # Disable DAgger
        self.algorithm.teacher_coef = None   # No teacher coefficient
        self.algorithm.teacher_loss_coef = None # No teacher loss

@configclass
class YourRobotNameTrainDAggerPPORunnerCfg(YourRobotNameFlatRefNoDAggerPPORunnerCfg):
    def __post_init__(self):
        super().__post_init__()
        self.max_iterations = 50000
        self.experiment_name = "your_robot_dagger"
        self.algorithm.amp_cfg.amp_update_interval = 50    # Slower AMP updates
        self.algorithm.amp_cfg.amp_pretrain_steps = 2000   # More pre-training

🎯 Key Configuration Principles​

📊 Network Architecture​

  • Transformer-based: ActorCriticMMTransformerV2 for sequence modeling
  • History Integration: Uses observation history for temporal patterns
  • Attention Mechanism: Multi-head attention for complex dependencies

🔄 Observation Processing​

  • Concatenation Groups: Organize related observations together
  • Phase Information: Gait phase signals for rhythmic behaviors
  • Reference Integration: Separate processing for reference observations

🎭 Advanced Features​

  • Symmetry Learning: Data augmentation with left-right mirroring
  • AMP Integration: Adversarial learning from motion capture data
  • DAgger Training: Teacher-student progressive learning
  • Curriculum Learning: Adaptive parameter schedules

💡 Critical Customizations for Your Robot​

  1. 🔢 AMP Input Dimension: Update backbone_input_dim based on your AMP observations
  2. 🎯 Observation Names: Replace phase and velocity term names with your robot's
  3. 📊 Network Size: Adjust dim_model, num_layers based on complexity
  4. ⏱️ Training Duration: Set max_iterations based on task difficulty
  5. 📂 Paths: Update DAgger checkpoint paths if using pre-trained models

✅ Configuration Checklist​

  • Basic PPO config created with simple algorithm parameters
  • Reference PPO config includes AMP, symmetry, and DAgger
  • Observation concatenation groups match your environment
  • AMP network dimensions align with observation extractors
  • Training iterations appropriate for environment complexity
  • Experiment names clearly identify configuration variants

🎉 Fantastic! Your intelligent agents are now configured and ready to learn! These configurations provide both basic RL capabilities and advanced imitation learning features that will make your robot truly intelligent! 🤖✨

🏆 Conclusion: Your Robot Training Configuration Complete!​

Congratulations, Robot Training Master! 🎓 You've just completed an epic journey from zero to hero in the world of sophisticated robot training with GBC! Let's celebrate what you've accomplished:

🌟 What You've Built​

You now possess a complete robot training ecosystem that rivals the most advanced research labs in the world:

🤖 Robot Foundation

  • ✅ Professional robot configuration with USD integration
  • ✅ Multiple actuator types (DC, MLP, Implicit) perfectly tuned
  • ✅ Physics-accurate simulation parameters

🌍 Training Environments

  • ✅ Rough Environment: Complex terrain with full challenge suite
  • ✅ Flat Environment: Simplified training ground for stable learning
  • ✅ DAgger Environment: Specialized imitation learning setup
  • ✅ Progressive Difficulty: From training wheels to expert level

🧠 Intelligent Agents

  • ✅ Standard PPO: Solid reinforcement learning foundation
  • ✅ Reference PPO: Advanced imitation learning with AMP, symmetry, and DAgger
  • ✅ Transformer Architecture: State-of-the-art sequence modeling
  • ✅ Curriculum Learning: Adaptive training that evolves with performance

🎯 Complete Integration

  • ✅ Gym Registration: Professional task discovery system
  • ✅ Modular Design: Flexible, extensible, and maintainable
  • ✅ Multi-Modal Learning: Seamless blend of RL and IL

🚀 Your Training Workflow​

Progressive Training Strategy

You're now equipped to train robots using this powerful progression:

  1. 🎯 Start with DAgger: Train precise motion tracking in controlled conditions
  2. 🏁 Move to Flat: Develop basic locomotion skills without terrain complexity
  3. ⛰️ Graduate to Rough: Master real-world challenges with terrain and obstacles
  4. 🌍 Deploy to Reality: Transfer learned skills to physical robots

💡 Pro Tips for Success​

Development Strategy

🔧 Development Approach:

  • Start simple, add complexity gradually
  • Test each component thoroughly before moving forward
  • Use flat environments for debugging and algorithm development
  • Save checkpoints regularly during long training runs
Hyperparameter Tuning

⚖️ Optimization Guidelines:

  • Begin with provided configurations as solid baselines
  • Adjust reward weights based on your robot's specific needs
  • Monitor training metrics to identify bottlenecks
  • Use curriculum learning to overcome difficult training phases
Advanced Features

🎭 Expert Techniques:

  • Leverage symmetry augmentation for more robust policies
  • Use AMP for natural, lifelike motion generation
  • Apply DAgger for rapid skill acquisition from demonstrations
  • Experiment with different network architectures for optimal performance

🌈 What's Next?​

Future Opportunities

Your robot training adventure doesn't end here! You're now ready to:

🔬 Research & Innovation

  • Experiment with novel reward structures
  • Develop custom observation processing
  • Create domain-specific physics modifiers
  • Contribute to the open-source robotics community

🏭 Real-World Applications

  • Deploy to physical robot platforms
  • Adapt for specific industrial applications
  • Scale to multi-robot systems
  • Integrate with vision and manipulation tasks

📚 Continuous Learning

  • Explore advanced RL algorithms
  • Study cutting-edge imitation learning techniques
  • Master domain randomization and sim-to-real transfer
  • Join the GBC community for collaboration and support

🎪 Final Words​

Congratulations!

You've just mastered one of the most sophisticated robot training frameworks available today. The combination of GBC's advanced features with IsaacLab's powerful simulation creates endless possibilities for robot intelligence.

Remember: Every expert was once a beginner. The comprehensive system you've built will serve as your launching pad for amazing discoveries and innovations in robotics. Whether you're training robots to walk, dance, manipulate objects, or explore new worlds, you now have the tools and knowledge to make it happen.

The future of robotics is in your hands! 🤖🚀

Happy Training, and may your robots learn fast! 🎯✨

🔗 What's Next? Ready to dive deeper? Check out how we run the training and deploys them to reality!