aloha-agilex embodiment, absolute 14-D joint control). A single multi-task policy trained on clean data only (50 tasks × 50 demo_clean demonstrations).demo_clean (100 rollouts per task, horizon = 37). Per-task results: RoboTwin_evaluation/README.md.| 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) |
1# 1. point the Evo-1 server at this checkpoint dir (Evo1_server.py, __main__ block: ckpt_dir)
2# 2. start the server
3cd Evo_1 && PYTHONPATH=. python scripts/Evo1_server.py
4# 3. copy the Evo-1 policy plugin into a RoboTwin checkout and run one task
5bash eval.sh place_burger_fries demo_clean step_20000 0 0 ws://0.0.0.0:9000 37arm_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.horizon = 37num_inference_timesteps = 50