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actor
dict
atom
dict
embodiment
dict
observation
dict
phase
dict
step
int64
tactile
dict
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46
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48
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50
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52
{"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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54
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56
{"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":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED)
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{ "id": 0, "is_boundary": 0, "name": [ 112, 114, 101, 95, 109, 111, 118, 101 ], "policy_step": -1, "sim_step": 58 }
58
{"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":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED)
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{ "id": 0, "is_boundary": 0, "name": [ 112, 114, 101, 95, 109, 111, 118, 101 ], "policy_step": -1, "sim_step": 60 }
60
{"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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{ "id": 0, "is_boundary": 0, "name": [ 112, 114, 101, 95, 109, 111, 118, 101 ], "policy_step": -1, "sim_step": 62 }
62
{"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":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED)
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{ "id": 0, "is_boundary": 0, "name": [ 112, 114, 101, 95, 109, 111, 118, 101 ], "policy_step": -1, "sim_step": 64 }
64
{"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.

OpenViTac Benchmark Dataset

OpenViTac is a visuo-tactile robot manipulation benchmark covering physical-property perception, fragile-object handling, contact-rich manipulation, and precise insertion. This release contains 1,300 simulated trajectories in HDF5 format across 11 tasks.

For the benchmark and data-collection details, see the paper and code repository.

Simulation tasks

Each task is stored under Simulation/GelSight_mini/<task>/. Some tasks have subdirectories for object properties or variants.

Capability Task directory Task summary Trajectories
Physical-property perception weight_classify Identify whether a block is light or heavy through interaction and place it on the matching target. 100
Physical-property perception hardness_classify Identify whether a block is soft or hard through contact and place it on the matching target. 100
Physical-property perception roughness_classify Identify whether a block is smooth or rough through contact and place it on the matching target. 100
Physical-property perception roughness_guided_regrasp Use surface roughness to decide whether to keep the grasped block or regrasp another one. 100
Physical-property perception empty_can_selection Distinguish an empty can from visually similar cans through interaction and place it in a basket. 100
Fragility-aware manipulation grasp_chip Grasp and move a fragile chip without damaging it. 100
Contact-rich manipulation gear_assembly Mount and rotate a gear while maintaining engagement with a paired gear. 100
Contact-rich manipulation pull_drawer Maintain a stable grasp on the handle while opening a drawer. 100
Precision manipulation insert_USB Align a USB connector with its slot and insert it. 100
Precision manipulation insert_block_v1 Align and insert a designated block into the matching opening. 100
Precision manipulation insert_block_v2 Select the instructed object, then insert it into the matching opening. 300

Real-world tasks

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Paper for FVL-Lab/OpenViTac