Complete Robot Learning Workflow¶

This page provides a comprehensive overview of the end-to-end robot learning workflow, from data collection to deployment.

Workflow Stages¶

graph TB
    A[1. Data Collection] --> B[2. Dataset Preparation]
    B --> C[3. Simulation Setup]
    C --> D[4. Model Training]
    D --> E[5. Evaluation]
    E --> F{Meets Requirements?}
    F -->|No| G[Debug & Iterate]
    G --> D
    F -->|Yes| H[6. Real Robot Testing]
    H --> I{Real-World Performance OK?}
    I -->|No| J[Collect More Data<br/>or Fine-tune]
    J --> A
    I -->|Yes| K[7. Production Deployment]
    K --> L[8. Monitoring & Maintenance]
    L --> M{Issues Detected?}
    M -->|Yes| J
    M -->|No| L

Stage 1: Data Collection¶

Goal: Gather high-quality demonstration data or prepare for environment interaction.

Key Activities: - Set up teleoperation systems - Collect expert demonstrations - Validate data quality - Ensure diversity of scenarios

Output: Raw demonstration trajectories

Time: 1-4 weeks

→ Data Collection Guide | → Teleoperation

Stage 2: Dataset Preparation¶

Goal: Convert raw data into standardized format for training.

Key Activities: - Convert to LeRobot format - Add metadata and annotations - Compute statistics for normalization - Split train/validation sets - Validate dataset integrity

Output: Structured dataset ready for training

Time: 3-7 days

→ LeRobot Format | → Format Specification

Stage 3: Simulation Setup¶

Goal: Configure simulation environment for safe, fast training.

Key Activities: - Choose simulator (IsaacSim/IsaacLab/Newton) - Configure robot and environment - Implement domain randomization - Verify physics accuracy - Set up parallel environments

Output: Validated simulation environment

Time: 1-2 weeks

→ Simulators Overview | → Comparison

Stage 4: Model Training¶

Goal: Train robot policy using appropriate learning method.

Approach Selection:

Vision-Language-Action (VLA)¶

Use when: Need language-conditioned control, multi-modal learning Time: 2-6 weeks Data needed: 1000-100k demonstrations with language → VLA Training

Reinforcement Learning (RL)¶

Use when: Can specify reward function, need optimization beyond demos Time: 1-4 weeks Data needed: Simulation environment + reward function → RL Training

Imitation Learning (IL)¶

Use when: Have demonstrations, reward hard to specify Time: 1-3 weeks Data needed: 50-10k demonstrations → IL Training

Output: Trained policy model

Stage 5: Evaluation¶

Goal: Thoroughly test policy before real-world deployment.

Key Activities: - Success rate measurement - Robustness testing with domain randomization - Failure mode analysis - Edge case testing - Performance benchmarking

Metrics to Track: - Success rate (primary metric) - Episode length (efficiency) - Action smoothness - Generalization to novel scenarios

Output: Evaluation report with metrics

Time: 3-7 days

→ Evaluation Guide | → Benchmarking

Stage 6: Real Robot Testing¶

Goal: Validate sim-to-real transfer on physical hardware.

Key Activities: - Safety checks and workspace boundaries - Gradual testing (static → slow → full speed) - Collect real-world performance data - Identify sim-to-real gaps - Fine-tune if necessary

Safety Checklist: - [ ] Emergency stop tested and working - [ ] Workspace boundaries configured - [ ] Collision detection enabled - [ ] Human supervisor present - [ ] Low-risk test scenarios first

Output: Real-world performance metrics

Time: 1-2 weeks

→ Sim-to-Real Transfer | → Safety

Stage 7: Production Deployment¶

Goal: Deploy policy to production robots.

Key Activities: - Model optimization (quantization, pruning) - Integration with robot control stack - Monitoring and logging setup - Gradual rollout - Documentation

Deployment Checklist: - [ ] Model optimized for target hardware - [ ] Latency meets real-time requirements - [ ] Failsafe mechanisms in place - [ ] Monitoring dashboards configured - [ ] Rollback plan prepared

Output: Production-ready system

Time: 1-3 weeks

→ Deployment Guide | → Edge Deployment

Stage 8: Monitoring & Maintenance¶

Goal: Ensure continued performance and improve over time.

Key Activities: - Monitor success rates - Collect failure cases - Periodic re-evaluation - Incremental improvements - Dataset updates

Monitoring Metrics: - Real-time success rate - Error types and frequencies - Performance degradation alerts - Hardware health

Output: Continuously improving system

→ Monitoring | → Production Systems

Iteration Loops¶

Short Loop (Training Iteration)¶

Train → Evaluate in Sim → Adjust hyperparameters → Retrain
Cycle time: Hours to days

Medium Loop (Sim-to-Real)¶

Train in Sim → Test on Real Robot → Fine-tune → Redeploy
Cycle time: Days to weeks

Long Loop (Dataset Improvement)¶

Deploy → Collect Failures → Add to Dataset → Retrain → Deploy
Cycle time: Weeks to months

Timeline Estimates¶

Fast Track (Simple Task)¶

Data Collection: 1 week
Dataset Prep: 2 days
Simulation: 3 days
Training (IL): 1 week
Evaluation: 3 days
Real Robot: 1 week
Deployment: 3 days Total: ~4 weeks

Standard (Moderate Complexity)¶

Data Collection: 2-3 weeks
Dataset Prep: 1 week
Simulation: 1-2 weeks
Training (VLA/RL): 2-4 weeks
Evaluation: 1 week
Real Robot: 2 weeks
Deployment: 1-2 weeks Total: ~10-15 weeks

Complex (Novel Task)¶

Data Collection: 4+ weeks
Dataset Prep: 1-2 weeks
Simulation: 2-3 weeks
Training (VLA): 4-8 weeks
Evaluation: 2 weeks
Real Robot: 3-4 weeks
Deployment: 2-3 weeks Total: ~18-26 weeks

Common Pitfalls & Solutions¶

Pitfall 1: Insufficient Data Diversity¶

Symptom: Good training performance, poor test performance Solution: Collect more diverse demonstrations, augment data

Pitfall 2: Sim-to-Real Gap¶

Symptom: Works in sim, fails on real robot Solution: Domain randomization, collect real-world fine-tuning data

Pitfall 3: Reward Hacking (RL)¶

Symptom: High reward, unintended behavior Solution: Constrain actions, add auxiliary rewards, use IL instead

Pitfall 4: Overfitting¶

Symptom: Perfect training, poor generalization Solution: More data, regularization, simpler model

Pitfall 5: Inefficient Training¶

Symptom: Training takes too long Solution: Parallelize environments, use faster simulator, smaller model

Best Practices¶

Start Simple: Begin with simple tasks before complex ones
Iterate Quickly: Fast feedback loops accelerate learning
Monitor Everything: Log all metrics for debugging
Safety First: Never skip safety checks
Validate Early: Test in sim before real robot
Document: Keep detailed records of experiments
Automate: Script repetitive tasks
Version Control: Track code, data, and model versions

Tools & Resources¶

Essential Tools¶

Dataset: LeRobot format
Simulation: IsaacSim, IsaacLab, or Newton
Training: PyTorch, Stable-Baselines3, Transformers
Evaluation: Weights & Biases, TensorBoard
Deployment: ONNX, TensorRT, Docker

Learning Resources¶

Next Steps¶

Ready to start? Choose your path: - Quick Start: Imitation Learning - Quick Start: Reinforcement Learning - Quick Start: VLA Models