Only RoboTwin and LIBERO evaluation adapters are included in this release.
RoboTwin is an external dependency. Set EVALUATION.robotwin_root to a checkout
that contains its standard policy directory.
Single-task example:
python experiments/robotwin/eval_robotwin_single.py \
EVALUATION.robotwin_root=external/RoboTwin \
EVALUATION.task_name=<task> \
EVALUATION.ckpt=<checkpoint.pt>The fixed-clock adapter submits joint targets at the trained control period
instead of relying on an unrelated controller integration schedule.
By default it resolves the released posttraining recipe from eval_robotwin;
set EVALUATION.training_config only when you need a run-specific resolved YAML.
The LIBERO runner starts two processes:
- a w0 policy server;
- a client linked against the external LIBERO environment.
Required inputs:
CKPT: w0 LIBERO posttraining checkpoint;W0_WAN_MODEL_DIR: Wan2.2 model directory;W0_LIBERO_STATS: matching normalization file;W0_LIBERO_TEXT_CACHE: matching UMT5 cache;LIBERO_PATH: external LIBERO checkout.
Example:
CKPT=assets/libero/checkpoint.pt \
W0_WAN_MODEL_DIR=/absolute/path/to/Wan2.2-TI2V-5B \
W0_LIBERO_STATS=assets/libero/normalization.json \
W0_LIBERO_TEXT_CACHE=assets/libero/text_embeds_cache \
LIBERO_PATH=external/LIBERO \
SUITE=libero_goal TASK_ID=0 NUM_TRIALS=50 \
bash scripts/run_libero_eval.shThe default evaluation executes a complete 16-action chunk unless
EXECUTE_N_ACTIONS is explicitly overridden. Changing this value changes the
closed-loop protocol and is not equivalent to training a different chunk-size
model.
A reproducible evaluation record should include:
- checkpoint path and step;
- Git commit or source snapshot hash;
- dataset/statistics fingerprint;
- suite and task IDs;
- environment seed and policy seed;
- number of executed actions per replan;
- inference steps;
- controller period;
- simulation version;
- camera transform and image codec;
- success criterion and trial count.