LIBERO-Plus OOD robustnessBrowse 284 families
LIBERO-Plus OOD robustness
Can the model plan, use tools, and finish a multi-step task?
Version / measure
- Domain
- Agents
- Ranked models
- 6
- Reported values
- 7
- Reports
- 1 · lab
- Direction
- Higher is better
- Status
- Version not specified
Rankings
Best reported score per model across effort settings
| # | Model | Best setting | Score |
|---|---|---|---|
| 1 | Qwen-RobotManipQwen | reportedcost n/a2 values · Jun 15 · source | 99.9% |
| 2 | StarVLAExternal baseline | reportedcost n/a1 value · Jun 15 · source | 95.7% |
| 3 | π₀.₅Physical Intelligence | reportedcost n/a1 value · Jun 15 · source | 94.1% |
| 4 | ABot-M0External baseline | reportedcost n/a1 value · Jun 15 · source | 91.6% |
| 5 | Being-H0.7External baseline | reportedcost n/a1 value · Jun 15 · source | 90.0% |
| 6 | π₀Physical Intelligence | reportedcost n/a1 value · Jun 15 · source | 81.4% |
Effort curve
Score vs. reported cost. Lines connect complete sweeps only.
No cost-linked effort sweep for this version.
Definition and comparison boundarypublic methodology
Can the model plan, use tools, and finish a multi-step task?
Agentic execution across tools, environments, or long-running workflows. Higher is better. The value is the percentage reported in this lab's table.
Harness, tools, prompts, attempt count, and benchmark version can materially change the result. Compare within one reporting context.
LIBERO-Plus evaluates camera, robot, language, lighting, background, noise, and layout perturbations; total is the source's aggregate.
Method / source ↗