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license: apache-2.0
language:
- en
library_name: lerobot
pipeline_tag: robotics
tags:
- lerobot
- robotics
- manipulation
- pi0
- single-arm
---
# pi05_single — insert_01, step 2000
openpi `pi05_single` checkpoint for a **single-arm, vision-only** insertion task.
Step 2000 (1.13 epochs), held-out validation loss **0.1063**.
This is the best checkpoint of the run -- validation loss got monotonically worse afterwards.
## Configuration
This run differs from the sibling `pi05_bi_*` models in two ways that make their
loss values **not comparable**:
| | `pi05_single` (this) | `pi05_bi` (two_tubes / task2 / bread) |
|---|---|---|
| state / action dim | 7 / 10 | bimanual dims |
| image streams used | **1** (`camera0` only) | 6 (2 RGB + 4 tactile) |
| lr schedule | constant 2e-4 after 1k warmup | cosine 2.5e-5 -> 2.5e-6 |
The dataset ships six image streams (`camera0`, `camera1`, and four tactile
sensors) but `pi05_single` reads only `camera0`; `camera1` and all tactile inputs
are discarded.
## Data
`KaiyueChen/insert_01`: 500 episodes / 252,795 frames, LeRobot v2.1, 30 fps,
images embedded in the parquet files.
**One frame per episode was dropped during preparation.** The collection pipeline
writes an all-zero action vector on each episode's terminal frame (it has no
successor action). Because the action vector contains two quaternions whose `w`
components sit at ~0.99999 with a q01-q99 spread of only 2e-4, quantile
normalization mapped those zeros to about -10,000, and the step-0 training loss
came out at **193,510** while validation read 1.78 (validation always draws the
same leading batches and happened to miss the terminal frames). Dropping the
terminal frame removes 500 of 252,795 frames (0.2%), all of them invalid, and
brings step-0 training loss to 0.74. Merged frame count: 252,295.
Split, held out per source (10%, seed 42): train 450 / val_seen 50 / val_unseen 50.
Normalization statistics (quantile q01/q99) over `train` only.
## Training
| | |
|---|---|
| config | `pi05_single` |
| hardware | 2 x A100-80GB, FSDP |
| batch size | 128 |
| this checkpoint | step 2000 (~1.13 epoch; 1 epoch = 1,764 steps) |
| run length | 6,000 steps (3.40 epochs), 4h08m at 2.4 s/it |
| lr | **constant 2e-4** after 1,000 warmup steps |
| | (`pi05_single` sets `peak_lr = decay_lr = 2e-4`, so the cosine schedule does not actually decay) |
| 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, same leading batches every evaluation.
| step | train | val_seen | val_unseen | gap |
|---:|---:|---:|---:|---:|
| 0 | 0.7447 | 0.7284 | 0.7377 | +0.0093 |
| **2000** | 0.0987 | 0.0860 | **0.1063** | +0.0203 |
| 4000 | 0.0881 | 0.0750 | 0.1164 | +0.0414 |
| 5999 | 0.0813 | 0.0673 | 0.1192 | +0.0519 |
`val_unseen` was already at its minimum by the first validation point (step 2000,
1.13 epochs) and got monotonically worse afterwards, while `val_seen` improved
monotonically. The gap grew from +0.0203 to +0.0519 (+156%) across three
consecutive points -- an unambiguous overfitting signature, not sampling noise.
For contrast, the sibling six-input `pi05_bi` runs on this codebase turned much
later and ended with far smaller gaps: two_tubes at ~2.5 epochs (+0.0220), task2
at ~2 epochs (+0.0041), bread at ~2.4 epochs (-0.0037). This run, with a single
camera and no tactile input, turned before 1.1 epochs and ended an order of
magnitude wider. The most likely explanation is the reduced observation space:
with one view and no touch, memorizing training trajectories is an easier way to
lower the loss than learning a transferable policy. `pi05_single_vitac`
(`camera0` + two tactile streams) would be the natural comparison, and insertion
is a task where contact feedback plausibly matters.
## Contents
```
checkpoint/
params/ # inference weights
train_state/ # optimizer state, for resuming
assets/insert_01/
norm_stats.json # computed over the train split only
```
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