| --- |
| license: apache-2.0 |
| task_categories: [robotics] |
| tags: [vla, pi0, openpi, robotwin, vla-mpc, eval, success-rate] |
| pretty_name: pi0 (Heisen0928/pi0_robotwin) RoboTwin 5-task eval — VLA vs VLA+MPC |
| --- |
| |
| # pi0 RoboTwin 5-task eval — `Heisen0928/pi0_robotwin`, VLA vs VLA+MPC |
| |
| Success-rate evaluation of the open-source **`Heisen0928/pi0_robotwin`** π0 |
| checkpoint on 5 RoboTwin tasks, 50 seeds each (seed 100000–100049), run through |
| the `vla_framework` eval pipeline. Two modes: raw policy (passthrough) and with |
| the reactive MPC corrector. |
|
|
| ## TL;DR |
|
|
| | task | passthrough | +MPC | Δ | |
| |---|---|---|---| |
| | place_container_plate | **29/50 (58%)** | 23/50 (46%) | −6 | |
| | place_empty_cup | **26/50 (52%)** | **35/50 (70%)** | **+9** | |
| | move_can_pot | **21/50 (42%)** | 13/50 (26%) | −8 | |
| | place_bread_skillet | 11/50 (22%) | 11/50 (22%) | 0 | |
| | move_pillbottle_pad | 10/50 (20%) | 8/50 (16%) | −2 | |
| | **TOTAL** | **97/250 (38.8%)** | **90/250 (36.0%)** | **−7 (−2.8pp)** | |
|
|
| **The reactive MPC is net-negative here (−2.8pp).** Only `place_empty_cup` |
| benefits (+9, a "close-but-incomplete" task where correction recovers grasps); |
| on tasks the raw policy already handles (plate, can_pot) the MPC perturbations |
| (`pos_std=0.015`, `rot_std=0.03`, gripper-flip) disrupt otherwise-successful |
| trajectories. MPC helps where the policy is marginal, hurts where it is already |
| competent. The MPC defaults are tuned for SmolVLA-style reactive correction; π0 |
| (continuous gripper, FK→ee path) needs a higher intervention threshold / smaller |
| perturbation. |
| |
| `Heisen0928/pi0_robotwin` is a **multi-task** π0 (no per-task finetune), hence |
| the modest 38.8% raw rate; plate/cup strongest (>50%), pillbottle/bread weakest |
| (~20%). |
|
|
| ## Setup (reproducible) |
|
|
| - **Checkpoint**: `Heisen0928/pi0_robotwin` (JAX/orbax, step 30000) → |
| converted to PyTorch with openpi's `convert_jax_model_to_pytorch.py` |
| (`--config_name pi0_aloha`, architecturally identical: action_dim 32, |
| action_horizon 50, gemma_2b + gemma_300m, bf16). norm_stats from |
| `30000/assets/robotwin_50_clean/` placed at |
| `physical-intelligence/robotwin/norm_stats.json`. |
| - **Backend**: in-process Embodied-AI VLAPredictor (`model_type: pi0`, |
| `config_name=pi0_robotwin`), JOINT(qpos,14D) policy bridged to the ee eval |
| path via RoboTwin FK (`fk: joint_to_ee`). |
| - **Poses**: `pose_source: robotwin_gt` (GT, no poselib — isolates the policy). |
| - **Env**: conda `vla_unified`; `MUJOCO_GL=egl`, `CUDA_VISIBLE_DEVICES=1`, |
| `EMBODIED_SKIP_TRANSFORMERS_CHECK=1`. subprocess-per-episode (SAPIEN leak). |
| - **Per-episode**: `--num_episodes 1 --max_steps 300`. |
|
|
| Configs in `configs/` (`pi0_robotwin_pass.local.yaml`, |
| `pi0_robotwin_mpc.local.yaml`). |
|
|
| ## Contents |
|
|
| - `results_per_seed.csv` — per task × seed, passthrough & mpc outcome |
| (SUCCESS/FAIL), 250 rows. |
| - `SUMMARY_pass.txt`, `SUMMARY_mpc.txt` — per-task aggregates. |
| - `configs/` — the exact eval configs (task overridden per run via `--task`). |
|
|
