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actor
dict
atom
dict
embodiment
dict
observation
dict
step
int64
tactile
dict
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250
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252
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{"left_tactile":{"depth":[[34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.(...TRUNCATED)
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256
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258
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260
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262
{"left_tactile":{"depth":[[34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.(...TRUNCATED)
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264
{"left_tactile":{"depth":[[34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.(...TRUNCATED)
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266
{"left_tactile":{"depth":[[34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.(...TRUNCATED)
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{"ee":[0.7004494667053223,6.300854693108704e-6,0.26420941948890686,0.0000266557362920139,0.999999105(...TRUNCATED)
{ "head": { "rgb": [ 255, 216, 255, 224 ] }, "wrist": { "rgb": [ 255, 216, 255, 224 ] } }
268
{"left_tactile":{"depth":[[34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,34.(...TRUNCATED)
End of preview.

UniVTAC Benchmark Dataset

The UniVTAC Benchmark dataset provides simulation data for tactile-based robotic manipulation tasks.

Overview

This dataset contains 100 episodes per task, totaling 800 episodes across 8 diverse manipulation tasks.

Task Gallery

UniVTAC Benchmark currently includes the following manipulation tasks, all featuring tactile sensing:

Task Module Description
Collect collect Collect contact-rich tactile data for pretraining
Lift Bottle lift_bottle Grasp and lift a bottle off a surface near a wall
Lift Can lift_can Grasp and lift a cylindrical can
Insert HDMI insert_HDMI Insert an HDMI connector into a port
Insert Hole insert_hole Precision peg-in-hole insertion
Insert Tube insert_tube Insert a tube into a fixture
Pull Out Key pull_out_key Extract a key from a lock
Put Bottle in Shelf put_bottle_in_shelf Place a bottle onto a shelf
Grasp & Classify grasp_classify Grasp an object and classify it by tactile feedback

Usage

For detailed instructions on data loading, environment setup, and benchmarking protocols, please visit our:
👉 official website 👉 Github repo

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