version: "1.0.1" name: physicalai-train-benchmarking-a-policy description: Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning rollout/episode/env settings, recording rollout videos, or interpreting results.json / results.csv. license: Apache-2.0

Benchmarking a Studio Policy

Benchmarking evaluates a trained policy by rolling it out in a gym and scoring success. Benchmark classes live in library/src/physicalai/benchmark/gyms/benchmark.py (Benchmark, PushTBenchmark, LiberoBenchmark); results types in benchmark/gyms/results.py (BenchmarkResults, TaskResult); rollout logic in library/src/physicalai/eval/rollout.py (evaluate_policy). The library supports both direct Python API use and the physicalai benchmark CLI wrapper (library/src/physicalai/cli/benchmark.py).

Python API invocation

Use this path for notebooks, tests, custom scripts, or direct library integrations.

from physicalai.benchmark.gyms import PushTBenchmark
from physicalai.policies import ACT
policy = ACT.load_from_checkpoint("experiments/act/version_0/checkpoints/last.ckpt")
benchmark = PushTBenchmark(num_episodes=1)
results = benchmark.evaluate(policy)
print(results.summary())
results.to_json("results/benchmark/results.json")
results.to_csv("results/benchmark/results.csv")

For exported artifacts, load the Runtime-facing model first:

from physicalai.benchmark.gyms import PushTBenchmark
from physicalai.inference import InferenceModel
model = InferenceModel("./exports/act_policy")
results = PushTBenchmark(num_episodes=1).evaluate(model)

CLI invocation

physicalai benchmark \
  --config configs/benchmark/pusht.yaml \
  --policy physicalai.policies.ACT \
  --ckpt_path experiments/act/version_0/checkpoints/last.ckpt \
  --output_dir ./results/benchmark

• --policy — policy class path.
• --ckpt_path — a .ckpt or an export directory.
• --config — a benchmark config (configs/benchmark/pusht.yaml, configs/benchmark/libero.yaml) selecting the Benchmark class and its settings.
• --output_dir — defaults to ./results/benchmark.

Override benchmark settings on the CLI, e.g. --benchmark.num_episodes 10 --benchmark.num_envs 8.

Output

• Prints results.summary() to stdout.
• Writes results.json and results.csv into --output_dir.
• Optional video via config video_dir + record_mode (all | failures | successes | none).

Workflow

1. Choose API or CLI deliberately. Use the Python API for code-level tasks; use CLI for config/docs/entry-point tasks.

• Done when: the selected path matches the user's requested surface area.

1. Confirm the policy loads from the checkpoint/export before a full sweep:

``bash physicalai benchmark --config configs/benchmark/<suite>.yaml --policy <ClassPath> --ckpt_path <path> --benchmark.num_episodes 1 ``

• Done when: one episode runs end-to-end and a summary prints.

1. Run the full benchmark with the intended episode/env counts.

• Done when: results.json and results.csv are written and the success metric is populated.

1. Interpret results via BenchmarkResults/TaskResult fields; compare against a baseline checkpoint on the same config.
2. Record videos for qualitative review when a task regresses (record_mode: failures).

Adding or changing a Benchmark

1. Subclass Benchmark in benchmark/gyms/ (study PushTBenchmark / LiberoBenchmark); the gym itself comes from physicalai.gyms (pusht.py, libero.py, …).
2. Add a matching config in library/configs/benchmark/.
3. Add tests under library/tests/unit/benchmark/.

• Done when: uv run --no-sync pytest tests/unit/benchmark passes and a 1-episode run succeeds.

Required checks

• The policy runs from both a .ckpt and an export dir if both are supported paths.
• The Python API path (Benchmark(...).evaluate(...)) and CLI wrapper agree on supported inputs for user-facing benchmark changes.
• Success/episode metrics are populated (not zero/NaN by accident) and reproducible across runs.
• Env/episode counts match hardware; large num_envs fits memory.
• Heavy gym deps (e.g. libero, robocasa) are gated behind their optional extras and imported lazily.

Related skills

• physicalai-train-training-a-policy — to produce the checkpoint being benchmarked.
• physicalai-train-exporting-and-validating — when benchmarking an exported artifact for deployment parity.