---
license: apache-2.0
language:
- en
library_name: lerobot
pipeline_tag: robotics
tags:
- lerobot
- robotics
- manipulation
- pi0
- tactile
- bimanual
---

# pi05_bi — two_tubes (01+02 merged), step 20000

openpi `pi05_bi` checkpoint for a bimanual two-tube pick-and-place task with
tactile inputs. Step 20000 (3.55 epochs), held-out validation
loss **0.0567**.

**This checkpoint is past the optimum and is not recommended for use.** The best of the run is step 14000 at 0.0537 (`Aether258/pi05_bi_two_tubes_0102_step14000`). This one is published as a reference point for what the model looks like after generalization has degraded.

## Task

Single unified instruction for every episode:

> Firstly, use the left hand to pick up the blue tube, and then use the right
> hand to pick up the green tube. Next, use the left hand to place the blue tube
> back firstly, and then use the right hand to place the green tube back.

## Data

| source | episodes | frames |
|---|---:|---:|
| `KaiyueChen/two_tubes_01` | 519 | 425,115 |
| `KaiyueChen/two_tubes_02` | 500 | 377,604 |
| **merged** | **1,019** | **802,719** |

LeRobot v2.1, 30 fps, `robot_type=bimanual`, images embedded in the parquet
files. Six camera streams: `camera0`, `camera1`, and four tactile sensors
(`tactile_left_0/1`, `tactile_right_0/1`).

Split, held out **per source repo** (10%, seed 42): train 917 / val_seen 102 /
val_unseen 102. Normalization statistics (quantile q01/q99) over `train` only.

## Training

| | |
|---|---|
| config | `pi05_bi` |
| hardware | 2 x A100-80GB, FSDP |
| batch size | 128 |
| this checkpoint | step 20000 (~3.55 epoch; 1 epoch = 5,639 steps) |
| lr | cosine decay, 1,000 warmup steps: peak 2.5e-5 -> 2.5e-6 over 30,000 steps |
| | (`CosineDecaySchedule` defaults -- `pi05_bi` does not override `lr_schedule`) |
| LoRA | rank 16 on the LLM, rank 32 on the action expert |
| vision tower | **fully fine-tuned** -- the freeze filter matches only `.*llm.*` |

The run spans three hosts, chained with `--resume` at steps 10000 and 16000.
`--resume` restores weights and optimizer state but **not** the data loader
position, so the batch sequence after each restart differs from what an
uninterrupted run would have seen.

## Validation curve

Flow-matching loss, 20 batches per split, same leading batches every evaluation.

| step | train | val_seen | val_unseen | gap | run |
|---:|---:|---:|---:|---:|---|
| 0 | 0.5525 | 0.4968 | 0.5261 | 0.0293 | hvxln3lg |
| 2000 | 0.0553 | 0.0504 | 0.0608 | 0.0104 | hvxln3lg |
| 4000 | 0.0490 | 0.0467 | 0.0576 | 0.0109 | hvxln3lg |
| 6000 | 0.0460 | 0.0437 | 0.0543 | 0.0106 | hvxln3lg |
| 8000 | 0.0441 | 0.0423 | 0.0550 | 0.0127 | hvxln3lg |
| 10000 | 0.0435 | 0.0416 | 0.0542 | 0.0126 | hvxln3lg / resume point |
| 12000 | 0.0420 | 0.0403 | 0.0538 | 0.0135 | q9ymwova |
| 14000 | 0.0404 | 0.0387 | 0.0537 | 0.0150 | q9ymwova |
| 16000 | 0.0395 | 0.0383 | 0.0551 | 0.0168 | q9ymwova / resume point |
| 18000 | 0.0383 | 0.0374 | 0.0563 | 0.0189 | i5s1jqvg |
| **20000** | 0.0376 | 0.0363 | **0.0567** | 0.0204 | i5s1jqvg |
| 22000 | 0.0361 | 0.0363 | 0.0584 | 0.0221 | i5s1jqvg |

`val_unseen` bottomed at step 14000 (0.0537) and rose monotonically afterwards --
0.0551, 0.0563, 0.0567, 0.0584 -- while `val_seen` kept improving and the gap grew
monotonically from 0.0150 to 0.0220. Five consecutive points moving the same
direction; this is unambiguous overfitting, not the noisy plateau seen between
steps 6000 and 14000. Training was stopped at step ~23400.

The step-16000 row is taken from the training host's local log: that host was shut
down before wandb flushed its final metrics, so the cloud run `q9ymwova` ends at
step 15800.

Note the training loss is measured on **augmented** images (random crop to 95%,
+-5 deg rotation, colour jitter, on all six streams including the four tactile
ones) while validation runs with `train=False`, which skips augmentation. The two
columns are therefore not directly comparable.

## Contents

```
checkpoint/
  params/                      # inference weights
  train_state/                 # optimizer state, for resuming
  assets/two_tubes_0102/
    norm_stats.json            # computed over the train split only
```