OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework
Paper • 2610.10384 • Published
Error code: JobManagerCrashedError
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
actor dict | atom dict | embodiment dict | observation dict | phase dict | step int64 | tactile dict |
|---|---|---|---|---|---|---|
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 1,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 46
} | 46 | {"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 0,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 48
} | 48 | {"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 0,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 50
} | 50 | {"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 0,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 52
} | 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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 0,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 54
} | 54 | {"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"id": 0,
"is_boundary": 0,
"name": [
112,
114,
101,
95,
109,
111,
118,
101
],
"policy_step": -1,
"sim_step": 56
} | 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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"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) |
{"basket":[0.5,-0.20000000298023224,0.0020000000949949026,1.0,-5.551115123125783e-17,0.0,1.040834085(...TRUNCATED) | {
"id": 2,
"tag": [
111,
112,
101,
110,
95,
103,
114,
105,
112,
112,
101,
114,
95,
102,
111,
114,
95,
101,
109,
112,
116,
121,
95,
99,
97,
110,
95,
115,
101,
97,
114,
99,
104
]
} | {"ee":[0.4807071387767792,-2.3673906923704635e-7,0.29937922954559326,0.00044410111149773,0.999553620(...TRUNCATED) | {
"head": {
"rgb": [
255,
216,
255,
224
]
},
"wrist": {
"rgb": [
255,
216,
255,
224
]
}
} | {
"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) |
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.
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 |