RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
Paper • 2610.03538 • Published
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action list | alert float32 | observations dict | provenance dict |
|---|---|---|---|
[0.41662299633026123,0.22563199698925018,0.14395900070667267,-0.010906434617936611,-0.99993848800659(...TRUNCATED) | 0 | {"images":{"cam_high":[[[127,131,135],[127,131,135],[127,131,135],[128,132,136],[128,132,136],[127,1(...TRUNCATED) | {"action_monotonic_ns":320917870724062,"action_sync_index":3,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.4166240096092224,0.22563199698925018,0.14395900070667267,-0.012902287766337395,-0.999914646148681(...TRUNCATED) | 0 | {"images":{"cam_high":[[[129,130,135],[129,130,135],[130,131,136],[130,131,136],[130,131,136],[129,1(...TRUNCATED) | {"action_monotonic_ns":320917904300453,"action_sync_index":4,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.4166249930858612,0.2256309986114502,0.14395900070667267,-0.016913829371333122,-0.9998546838760376(...TRUNCATED) | 0 | {"images":{"cam_high":[[[129,130,133],[129,130,133],[130,131,134],[130,131,134],[129,130,133],[129,1(...TRUNCATED) | {"action_monotonic_ns":320917937457999,"action_sync_index":5,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.4166260063648224,0.2256300002336502,0.14395900070667267,-0.01867954432964325,-0.9998232126235962,(...TRUNCATED) | 0 | {"images":{"cam_high":[[[129,130,133],[129,130,133],[130,131,134],[130,131,134],[130,131,134],[129,1(...TRUNCATED) | {"action_monotonic_ns":320917970729998,"action_sync_index":6,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.4166260063648224,0.2256300002336502,0.14395900070667267,-0.021341001614928246,-0.9997698664665222(...TRUNCATED) | 0 | {"images":{"cam_high":[[[127,131,135],[127,131,135],[127,131,135],[128,132,136],[128,132,136],[127,1(...TRUNCATED) | {"action_monotonic_ns":320918004133269,"action_sync_index":7,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.41662800312042236,0.22562900185585022,0.14395900070667267,-0.02645951509475708,-0.999647259712219(...TRUNCATED) | 0 | {"images":{"cam_high":[[[127,132,131],[127,132,131],[128,133,132],[128,133,132],[128,133,132],[127,1(...TRUNCATED) | {"action_monotonic_ns":320918037430858,"action_sync_index":8,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.41662898659706116,0.22562800347805023,0.14395900070667267,-0.029249422252178192,-0.99956941604614(...TRUNCATED) | 0 | {"images":{"cam_high":[[[127,131,133],[127,131,133],[128,132,134],[128,132,134],[127,131,133],[127,1(...TRUNCATED) | {"action_monotonic_ns":320918071344791,"action_sync_index":9,"action_xarm_causal_fill":0,"action_xar(...TRUNCATED) |
[0.41662898659706116,0.22562800347805023,0.14395900070667267,-0.031245499849319458,-0.99950897693634(...TRUNCATED) | 0 | {"images":{"cam_high":[[[129,130,135],[129,130,135],[130,131,136],[130,131,136],[130,131,136],[129,1(...TRUNCATED) | {"action_monotonic_ns":320918104056385,"action_sync_index":10,"action_xarm_causal_fill":0,"action_xa(...TRUNCATED) |
[0.4166310131549835,0.22562700510025024,0.14395900070667267,-0.03643949329853058,-0.999332845211029,(...TRUNCATED) | 0 | {"images":{"cam_high":[[[129,130,135],[129,130,135],[129,130,135],[130,131,136],[130,131,136],[129,1(...TRUNCATED) | {"action_monotonic_ns":320918137390912,"action_sync_index":11,"action_xarm_causal_fill":0,"action_xa(...TRUNCATED) |
[0.4166319966316223,0.22562600672245026,0.14395900070667267,-0.03923647850751877,-0.9992268681526184(...TRUNCATED) | 0 | {"images":{"cam_high":[[[127,131,133],[127,131,133],[127,131,133],[128,132,134],[128,132,134],[127,1(...TRUNCATED) | {"action_monotonic_ns":320918170775386,"action_sync_index":12,"action_xarm_causal_fill":0,"action_xa(...TRUNCATED) |
End of preview.
RATE Real-World Training Data
Real-world robot training data used by RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation.
The public release contains exactly 50 episodes for each task:
| Directory | Task | Episodes |
|---|---|---|
usb_insertion/ |
USB insertion | episode_0.hdf5–episode_49.hdf5 |
plug_insertion/ |
Plug insertion | episode_0.hdf5–episode_49.hdf5 |
bottle_cap_screwing/ |
Bottle-cap screwing | episode_0.hdf5–episode_49.hdf5 |
Each episode uses an EEF10 state/action representation and contains two camera RGB streams, two tactile RGB streams, and a continuous training-only /alert label in [0, 1]. Bottle-cap screwing additionally contains dual tactile-depth supervision. Alert is not an inference input.
The files are stored as HDF5 and are organized by task. Episode numbering is kept unchanged from the verified RATE delivery dataset.
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