EurekaZang123/Kino-Fail
Kino-Fail Kino-Fail is a counterfactual benchmark for recovery-relevant failure attribution in quadrupedal navigation. The ICRA paper-facing release contains 11 physics and sensing interventions, 191 RTX/PBR scenes, 11,418 matched groups, and 22,836 accepted physical units. Release status: complete. Data organization data/scale/<scene>/: five-frame front-camera RGB and 21x19 proprioception windows. data/conflict/<scene>/: T2 visual-decisive and T3… See the full description on the dataset page: https://huggingface.co/datasets/EurekaZang123/Kino-Fail.
Kino-Fail
Kino-Fail is a counterfactual benchmark for recovery-relevant failure attribution in quadrupedal navigation. The ICRA paper-facing release contains 11 physics and sensing interventions, 191 RTX/PBR scenes, 11,418 matched groups, and 22,836 accepted physical units.
Release status: complete.
Data organization
data/scale/<scene>/: five-frame front-camera RGB and 21x19 proprioception windows.data/conflict/<scene>/: T2 visual-decisive and T3 proprioception-decisive snapshots.features/: the frozen all-191 feature matrices used for the reported benchmark.actions/: formal branched recovery trajectories.metadata/: official unit index, scene registry, audits, and benchmark reports.
Each appearance view belongs to its underlying physical episode and is not an independent physical sample. Evaluation should use the official physical-unit index in metadata/benchmark/physical_units.jsonl.
Scope
This release contains generated sensor observations, proprioceptive trajectories, labels, and experiment metadata. It excludes launcher logs, crash material, local filesystem paths, simulator caches, source .blend/.usd assets, and superseded development corpora. Unitree and Isaac Sim assets are not redistributed.
Code
Generation and evaluation code: https://github.com/EurekaZang/KinoVLA