---
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`).