The missing layer between W&B and Datadog: observability for AI robots
The article discusses the need for improved observability in robotics, particularly for AI robots. It highlights the challenges faced when debugging robotic failures, which differ from traditional software observability. The author argues that a new framework is necessary to capture and analyze robotic episodes effectively.
- ▪Robotics observability must focus on episodes rather than individual log lines.
- ▪Successful debugging requires synchronized data from video, sensors, and actions, all tagged for reproducibility.
- ▪The article introduces a new approach to logging and analyzing robotic runs to enhance reliability and safety.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3904904) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Art Levitt Posted on May 29 The missing layer between W&B and Datadog: observability for AI robots #robotics #ai #observability #mlops A backend service falls over at 2am and you know the drill: open the dashboard, follow the trace, find the bad deploy, roll back. Twenty years of tooling (logs, metrics, traces, APM) exists to answer "what just happened, and why?" Now your robot bricks a grasp at 2am. What do you open? There's no trace.
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