robotics
The Average Hid Where the Robot Would Fail
SCAPE predicted scenario-specific real-world policy performance from limited paired trials and large simulation runs.
Summary
SCAPE predicted scenario-specific real-world policy performance from limited paired trials and large simulation runs.
SCAPE corrects simulation labels for sim-to-real bias, then uses conformal prediction to estimate real-world policy performance for particular deployment scenarios. In autonomous-driving and quadruped studies, the authors report lower scenario-level error than neural and aggregate baselines, narrower calibrated intervals and better out-of-distribution behavior. A physical Unitree Go2 test supports the evaluation method on one platform; it does not certify a policy for unrestricted deployment.
Why it matters
SCAPE predicted scenario-specific real-world policy performance from limited paired trials and large simulation runs.
Limits and context
- A physical Unitree Go2 test supports the evaluation method on one platform; it does not certify a policy for unrestricted deployment.
Key claims
SCAPE predicted scenario-specific real-world policy performance from limited paired trials and large simulation runs.
Qualification: A physical Unitree Go2 test supports the evaluation method on one platform; it does not certify a policy for unrestricted deployment.
Evidence: source-2026-08-21-003
Sources
- arXiv preprint 2608.19425arXiv · primary research
Corrections
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