VLA-Powered LEGO Quality Inspection Robot
Our system combines SmolVLA and ACT (Action Chunking with Transformers) to learn robot movements from a human-operated leader arm. Using Physical AI, we collect real-world demonstration data by guiding the robot through LEGO inspection and sorting tasks. The robot learns to identify LEGO blocks, determine whether each piece is perfect or defective, and autonomously pick and place it onto the corresponding plate. We use an H100 GPU for imitation-learning training, enabling the model to learn manipulation policies from the collected demonstrations. To make the system easier to understand and troubleshoot, we also built a debugging and visualization tool on Rerun.io, allowing us to replay demonstrations, inspect robot actions, and analyze model behavior step by step. Pipeline: Human Leader Arm → Physical AI Data Collection → SmolVLA + ACT → H100 Imitation Training → Robot Pick & Place → Perfect / Defective Plate → Rerun.io Debug & Replay
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