---
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 6000

openpi `pi05_bi` checkpoint from a bimanual two-tube pick-and-place run with
tactile inputs. This is the step-6000 checkpoint, which had the lowest held-out
validation loss of the run up to step 8000.

## 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.

`two_tubes_02` shipped with the placeholder string `"perform manipulation task"`
in its `meta/tasks.jsonl`. Since `prompt_from_task=True` feeds this string
straight to the model, the two sources were forced onto the single instruction
above at merge time -- otherwise the model would be taught that two different
instructions mean the same motion.

## 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 (`total_videos=0`). Six camera streams: `camera0`, `camera1`, and four
tactile sensors (`tactile_left_0/1`, `tactile_right_0/1`).

### Split

Episodes are held out **per source repo** (10%, seed 42) so the held-out set
keeps the same source mix as train:

| split | episodes |
|---|---:|
| `train` | 917 |
| `val_seen` (subset of train) | 102 |
| `val_unseen` (held out) | 102 |

Normalization statistics (quantile q01/q99) are computed over the `train` split
only.

## Training

| | |
|---|---|
| config | `pi05_bi` |
| hardware | 2 x A100-80GB, FSDP |
| batch size | 128 |
| this checkpoint | step 6000 (~1.06 epoch; 1 epoch = 5,639 steps) |
| planned length | 20,000 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`; the `peak_lr=2e-4 / decay_steps=100000` block in `config.py` is referenced only by `pi05_single*`) |
| LoRA | rank 16 on the LLM, rank 32 on the action expert |
| vision tower | **fully fine-tuned** -- the freeze filter matches only `.*llm.*` |

## Validation curve

Flow-matching loss, 20 batches per split, evaluated on the same leading batches
each time so successive points are comparable.

| step | train | val_seen | val_unseen | gap |
|---:|---:|---:|---:|---:|
| 0 | 0.5525 | 0.4968 | 0.5261 | 0.0293 |
| 2000 | 0.0504 | 0.0504 | 0.0608 | 0.0104 |
| 4000 | 0.0475 | 0.0467 | 0.0576 | 0.0109 |
| **6000** | 0.0448 | 0.0437 | **0.0543** | 0.0105 |
| 8000 | 0.0439 | 0.0423 | 0.0550 | 0.0127 |

`val_unseen` fell monotonically through step 6000, then ticked up by 0.0007 at
step 8000 while `val_seen` kept improving and the gap widened. Each validation
pass covers only ~2,560 frames (roughly 3-4 episodes of ~780 frames), so a
change of that size is within noise -- whether step 6000 is the true optimum is
not yet settled.

## Contents

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