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actor dict | atom dict | embodiment dict | observation dict | step int64 | tactile dict |
|---|---|---|---|---|---|
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{"prism":[0.6999546885490417,-6.818879683123669e-6,0.051223523914813995,0.9999992251396179,-0.001061(...TRUNCATED) | {
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{"prism":[0.6999567151069641,-8.775536116445437e-6,0.05122093856334686,0.9999992847442627,-0.0010666(...TRUNCATED) | {
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} | 260 | {"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) |
{"prism":[0.6999716758728027,-9.71129338722676e-6,0.051217932254076004,0.9999992847442627,-0.0010607(...TRUNCATED) | {
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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) |
{"prism":[0.6999824047088623,-7.279524652403779e-6,0.051209550350904465,0.9999992847442627,-0.001040(...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) |
{"prism":[0.6999952793121338,-3.4933302686113166e-6,0.051202867180109024,0.9999992847442627,-0.00101(...TRUNCATED) | {
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} | {"ee":[0.7003560662269592,6.243089956115e-6,0.2642815113067627,0.00002346690234844573,0.999999284744(...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) |
{"prism":[0.7000102996826172,1.681042022028123e-6,0.05119720846414566,0.9999992847442627,-0.00097206(...TRUNCATED) | {
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} | {"ee":[0.7004494667053223,6.300854693108704e-6,0.26420941948890686,0.0000266557362920139,0.999999105(...TRUNCATED) | {
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} | 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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