We built the first cross-sensor tactile annotation benchmark — and open-sourced the labeling tool too(reddit.com)

Hey r/robotics, I've been working with tactile sensor data for a while, and there's a problem that kept bugging me: every tactile dataset has its own format, its own sensor, and its own way of labeling things. Want to compare your material classification model on GelSight vs DIGIT vs DMA? Good luck — the annotations don't line up, the schemas are different, and there's no standard way to even represent a "tactile label" across sensors. So we built two things to fix this: **TLabel** — an open-source sensor-agnostic tactile data labeling toolkit (Python + Panel UI). It supports annotation, quality scoring, batch processing, and exports to HDF5/CSV/JSON. Think of it as LabelImg but for touch data. **TLabel-Bench** — the first cross-sensor tactile annotation benchmark. It provides: - Unified JSON annotation schema that works across GelSight, DIGIT, and DMA sensors - Standardized evaluation scripts for material classification, episode segmentation, and cross-sensor transfer - Compatible with existing datasets (Touch and Go, SSVTP, ObjTac, Daimon-Infinity, CLAMP) The key idea: we only distribute annotation files + download scripts, not the raw data itself. This respects the original datasets' licenses while still letting researchers work with standardized labels. Why this matters: tactile sensing is moving fast (GelSight, DIGIT, DMA, Xense, etc.), but the tooling for *labeling and comparing* across sensors basically doesn't exist. Every paper re-implements their own annotation pipeline. We're trying to fix that. Would love feedback from anyone working with tactile data. What sensors are you using? What labeling tasks do you need? GitHub: - https://github.com/liesliy/tlabel (labeling toolkit, pip install tlabel) - https://github.com/liesliy/tlabel-bench (benchmark + evaluation) submitted by /u/ImmediateArm7942 [link] [Kommentare]