Debugging Robot Arm Failures with LeRobot SO-100 & dFL скачать в хорошем качестве

Debugging Robot Arm Failures with LeRobot SO-100 & dFL

Robot fails “for no reason”? This demo shows how to turn that black box into clear, actionable insight. We load the LeRobot SO-100 sorting dataset from Hugging Face into dFL by Sophelio, visualize commanded actions versus observed joint positions, and reveal where the policy is moving faster than the hardware can follow. From there, we: Manually label misalignment regions on the elbow joint Deploy a custom, rolling-window misalignment autolabeler Run bulk autolabeling across all episodes to find every failure window Export a curated golden dataset for retraining or PID tuning If you’re building robot arms, tuning controllers, or working with LeRobot datasets, this walkthrough shows how dFL can compress hours of log-diving into a repeatable, data-driven workflow. The same approach extends to many other robotics scenarios: multi-joint manipulators, mobile robots, sim-to-real studies, and hardware-in-the-loop experiments where you need to line up policies, sensor streams, and failures at scale. Anywhere you have time-series logs and messy episodes, dFL can help you visualize, label, and turn them into high-quality training and evaluation data. Learn more about dFL: https://dfl.sophelio.io