Evo-1 · RoboTwin Checkpoint
Evo-1 policy trained on RoboTwin 2.0 (50 bimanual manipulation tasks, aloha-agilex embodiment, absolute 14-D joint control). A single multi-task policy trained on clean data only (50 tasks × 50 demo_clean demonstrations).
Overall success rate: 65.7% on RoboTwin-50 demo_clean (100 rollouts per task, horizon = 37). Per-task results: RoboTwin_evaluation/README.md.
Files
| File | Description |
|---|---|
config.json | Evo-1 model config (vlm_name = OpenGVLab/InternVL3-1B) |
norm_stats.json | Per-task normalization stats — 50 keys robotwin_<task> under aloha_joint |
mp_rank_00_model_states.pt | Model weights (~1.5 GB) |
Usage
Full instructions: Evo-1 repo → 🧪 RoboTwin Benchmark.
# 1. point the Evo-1 server at this checkpoint dir (Evo1_server.py, __main__ block: ckpt_dir)
# 2. start the server
cd Evo_1 && PYTHONPATH=. python scripts/Evo1_server.py
# 3. copy the Evo-1 policy plugin into a RoboTwin checkout and run one task
bash eval.sh place_burger_fries demo_clean step_20000 0 0 ws://0.0.0.0:9000 37
arm_key / dataset_key are sent per-request by the RoboTwin client (aloha_joint + robotwin_<task>); the server reads them from the payload, so no server edit is needed for these.
⚠️ Evaluation recipe (all three matter)
horizon = 37num_inference_timesteps = 50- Gaussian action smoothing, kernel = 9