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Linked challenge: Sim-to-Real Drift Detection Pipeline for Legged Robots with LangGraph and Modal

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Linked challenge

Sim-to-Real Drift Detection Pipeline for Legged Robots with LangGraph and Modal

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Define a LangGraph `StateGraph` that transitions between three states: `analyze_logs`, `run_sim_comparison`, and `trigger_retraining`. Use a typed `TypedDict` for the graph state to track `drift_detected` and `model_uri`. Ensure the graph includes a conditional edge after `run_sim_comparison`.

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Linked challenge

Sim-to-Real Drift Detection Pipeline for Legged Robots with LangGraph and Modal

Following Boston Dynamics' deployment of legged robots at the FIFA World Cup and the rise of 'Contact Intelligence' in robotics, this challenge focuses on the critical 'sim-to-real' gap. You will build a stateful monitoring and retraining pipeline that detects when a robot's physical performance (real-world) deviates from its simulated expectations. Using LangGraph for workflow orchestration and Modal for scalable cloud inference/simulation, you will create a loop that: 1) Ingests telemetry logs from a legged robot, 2) Compares these logs against a 'Golden Simulation' run in a headless MuJoCo environment, 3) Triggers a fine-tuning job on Modal if the deviation (drift) exceeds a safety threshold. This architecture ensures that the robot's Reinforcement Learning policy remains robust as the physical environment or hardware components degrade over time.

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