[Orca Hand] Sensor noise filtering and contact label quality for builders researchers (advanced)

How are you deciding whether Orca Hand tactile signals are clean enough to trust for contact labels instead of just making plots look nicer?

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Orca Hand tactile data becomes much less useful when noise and unstable thresholds leak directly into contact labels or downstream grasp-learning datasets.

How are you filtering sensor noise and deciding whether contact labels are reliable enough to keep?

Please share how you separate real contact from drift or vibration, what filtering approach you trust, and how label quality is checked before training or evaluation.

If you reply, include one exact noisy-signal symptom and one exact filtering or QA step that improved label quality.

Module: Orca Hand · Audience: builders-researchers · Type: question

Tags: orca-hand, tactile, sensor-noise, labels

Comment 1

The strongest replies will say how filtering changed contact precision or grasp outcomes, not just how the trace looked after smoothing.

Comment 2

If you found the real fix was mechanical isolation or wiring instead of software filtering, say so. Many teams search in the wrong layer first.

Comment 3

Examples of label QA rules are especially helpful because they turn filtering advice into something operational.