subagent-copilot-cli

copilot --model gpt-4.1 ...

$result = copilot --model gpt-4.1 --allow-all -p "<prompt>" -s

# Add MotrixLab directories for context
copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\starter_kit\navigation1 -p "<prompt>" -s

# Multiple directories
copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\starter_kit --add-dir d:\MotrixLab\runs -p "<prompt>" -s

# Start interactive session for complex analysis
copilot --model gpt-4.1 --allow-all -i "Let's analyze the VBot navigation reward structure together"

# After view.py or train.py --render is running, capture window
# Use external screenshot tool, then analyze:
$analysis = copilot --model gpt-4.1 --allow-all -p "Look at d:\MotrixLab\screenshots\vbot_render.png. Describe: 1) Robot pose and stance 2) Terrain features visible 3) Distance from goal marker 4) Any collision or instability signs" -s

# Analyze multiple frames from a training run
$frames = copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\renders\episode_001 -p "Examine all PNG frames in this directory. Create a timeline of robot behavior: stance changes, terrain traversal progress, any falls or recovery attempts." -s

# Capture pre and post action states
$comparison = copilot --model gpt-4.1 --allow-all -p "Compare d:\MotrixLab\screenshots\before_stairs.png and d:\MotrixLab\screenshots\after_stairs.png. Did the VBot successfully ascend? What gait pattern is visible?" -s

# When robot falls or fails navigation
$diagnosis = copilot --model gpt-4.1 --allow-all -p "Look at d:\MotrixLab\screenshots\failure_frame.png. What caused the VBot to fail? Check: 1) Leg configuration 2) Body orientation 3) Terrain interaction 4) Likely cause of termination" -s

# Analyze competition instructions
$nav1_info = copilot --model gpt-4.1 --allow-all -p "Read d:\MotrixLab\starter_kit\navigation1\navigation1.pdf. Summarize: 1) Task objectives 2) Terrain description 3) Scoring criteria 4) Time limits 5) Robot constraints" -s

$nav2_info = copilot --model gpt-4.1 --allow-all -p "Read d:\MotrixLab\starter_kit\navigation2\navigation2.pdf. What are the key differences from navigation1? List new challenges." -s

# Side-by-side comparison
$comparison = copilot --model gpt-4.1 --allow-all -p "Read both navigation1.pdf and navigation2.pdf from d:\MotrixLab\starter_kit\. Create a comparison table of: terrain complexity, distance, time limits, scoring weights." -s

# If you exported TensorBoard plots as images
$rewards = copilot --model gpt-4.1 --allow-all -p "Look at d:\MotrixLab\analysis\reward_curve.png. Describe the training progress: 1) Convergence pattern 2) Reward scale 3) Any plateaus or instabilities 4) Estimated episodes to convergence" -s

# Multiple metric plots
$metrics = copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\analysis\tensorboard_exports -p "Examine all training plots. For each metric (episode_reward, policy_loss, value_loss, entropy): describe trend, identify anomalies, suggest hyperparameter adjustments." -s

# Compare two different hyperparameter settings
$comparison = copilot --model gpt-4.1 --allow-all -p "Compare reward curves: d:\MotrixLab\analysis\run_lr001.png vs d:\MotrixLab\analysis\run_lr0001.png. Which learning rate produces faster convergence? More stable training?" -s

# Deep dive into reward function design
$rewards = copilot --model gpt-4.1 --allow-all -p "Read d:\MotrixLab\starter_kit\navigation1\vbot\cfg.py. Analyze the RewardConfig class: 1) List all reward components with weights 2) Identify potential reward hacking risks 3) Suggest improvements for navigation task" -s

# Understand the neural network structure
$arch = copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\motrix_rl\src\motrix_rl -p "Find the PPO policy network definition. Describe: layer sizes, activation functions, observation preprocessing, action output format." -s

# Understand terrain and physics setup
$scene = copilot --model gpt-4.1 --allow-all -p "Read d:\MotrixLab\starter_kit\navigation1\vbot\xmls\scene_section001.xml. Describe: 1) Terrain geometry 2) Obstacle placements 3) Goal marker positions 4) Physics parameters" -s

# Ask subagent to research while you work on something else
$research = copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab -p "Research question: What curriculum learning strategies would help VBot learn stair climbing? Consider the reward structure in cfg.py and suggest a 3-stage curriculum." -s
Write-Host "Research findings: $research"

# Have subagent analyze your hypothesis
$test = copilot --model gpt-4.1 --allow-all -p "Hypothesis: The VBot fails on stairs because heading_tracking reward conflicts with position_tracking when approaching stairs at an angle. Analyze cfg.py reward weights and confirm or refute this." -s

# Connect project to RL best practices
$lit = copilot --model gpt-4.1 --allow-all -p "Compare the PPO hyperparameters in d:\MotrixLab\motrix_rl\src\motrix_rl\skrl\cfg.py against recommended settings from Schulman et al. (2017) and SKRL documentation. What should be adjusted for locomotion tasks?" -s

# Get analysis result and use it
$reward_analysis = copilot --model gpt-4.1 --allow-all -p "What is the termination penalty in navigation1 cfg.py?" -s

if ($reward_analysis -match "-200") {
    Write-Host "High termination penalty detected - robot will be conservative"
}

# First: identify the problem
$problem = copilot --model gpt-4.1 --allow-all -p "Look at d:\MotrixLab\screenshots\failure.png. What type of failure is this?" -s

# Second: suggest fix based on problem
$solution = copilot --model gpt-4.1 --allow-all -p "The VBot failure type is: $problem. What reward modifications in cfg.py would prevent this?" -s

# Generate and save detailed analysis
copilot --model gpt-4.1 --allow-all -p "Analyze d:\MotrixLab\starter_kit\navigation1\vbot\cfg.py thoroughly. Output a markdown report covering: environment overview, reward breakdown, training recommendations." -s > d:\MotrixLab\analysis\nav1_report.md

$errorDir = "d:\MotrixLab\debug\episode_failure_$(Get-Date -Format 'yyyyMMdd_HHmmss')"
New-Item -ItemType Directory -Path $errorDir -Force

# After capturing failure frames to $errorDir...
$diagnosis = copilot --model gpt-4.1 --allow-all --add-dir $errorDir -p "Examine all frames in this directory showing a failed episode. Create a failure timeline: 1) Initial state 2) Critical moment before failure 3) Failure frame 4) Root cause analysis" -s

Write-Host "Failure diagnosis: $diagnosis"

# Analyze locomotion quality
$gait = copilot --model gpt-4.1 --allow-all --add-dir d:\MotrixLab\renders\locomotion_test -p "Examine the sequence of VBot frames. Analyze gait quality: 1) Foot contact pattern 2) Stride symmetry 3) Body stability 4) Compare to typical quadruped trotting gait" -s