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Building Training Data Pipelines for Physical AI Systems

7 chapters · 35 lessons

1. 1. Multimodal Sensor Capture for Physical Tasks

   5 lessons

   Learn to design sensor arrays that record synchronized video, depth, motion, force, and context data from human demonstrations.

2. 2. Annotation Workflows for Action and Contact Data

   5 lessons

   Build labeling pipelines that segment tasks, mark object interactions, and capture success criteria for physical manipulation.

3. 3. Simulation and Synthetic Data Generation

   5 lessons

   Evaluate when and how to use physics simulators to augment real-world data with synthetic demonstrations and domain randomization.

4. 4. Data Preprocessing and Feature Engineering for Embodied AI

   5 lessons

   Transform raw sensor streams into model-ready representations that capture spatial relationships, temporal dynamics, and action affordances.

5. 5. Training Imitation and Reinforcement Learning Policies

   5 lessons

   Apply behavioral cloning, inverse reinforcement learning, and policy gradient methods to learn from demonstration data.

6. 6. Post-Deployment Feedback and Continuous Improvement

   5 lessons

   Design systems that collect deployment data, detect distribution drift, and retrain models to maintain performance in changing environments.

7. 7. Data Governance and Safety for Physical AI

   5 lessons

   Implement privacy, consent, and safety controls for training data that captures human motion, workplace routines, and safety-critical processes.