---
license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
tags:
  - robotics
  - lerobot
  - imitation-learning
  - act
  - b-spline
  - so101
  - bspline_act
datasets:
  - atharva-pantheon/pick_block_bowl
model_name: pickblock_bspline_act
---

# pickblock_bspline_act

An [ACT](https://huggingface.co/papers/2304.13705) policy that predicts **B-spline
trajectory segments** instead of discrete action chunks, trained on SO-101 black-cube
picking. This is the "Reg.+BSP" variant from
[B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations](https://arxiv.org/abs/2607.09648).

The policy outputs a curve, so executing `a(n·t)` replays the same trajectory geometry
n times faster without retraining. Speed-up is an inference flag, verified exact to
0.000e+00 deviation at 2× and 4×.

> [!WARNING]
> **This model is trained on 13 episodes and does not generalize well.** Held-out error
> is 22.8° against 6.2° on training data — a 3.7× gap that never closed across 40,000
> steps. It has never been run on hardware. Treat it as a pipeline validation artifact,
> not a working policy. See [Evaluation](#evaluation) and [Limitations](#limitations).

## Download

```bash
hf download Dimios45/pickblock_bspline_act --local-dir ./pickblock_bspline_act
```

The repository root holds the recommended checkpoint, so `--policy.path` also accepts
the repo id directly. Individual checkpoints live under `checkpoints/<step>/`.

## Run it

```bash
lerobot-rollout \
    --strategy.type=base \
    --policy.path=./pickblock_bspline_act/checkpoints/010000/pretrained_model \
    --policy.speed_up=1.0 \
    --device=cuda \
    --robot.type=so101_follower \
    --robot.port=/dev/ttyACM0 --robot.id=FOLLOWER \
    --robot.cameras='{front: {type: opencv, index_or_path: 0, width: 640, height: 360, fps: 30}, ee: {type: opencv, index_or_path: 4, width: 640, height: 360, fps: 30}}' \
    --task="Grab the black cube" \
    --fps=30 --duration=60
```

`--policy.path` takes either a Hub repo id (`namespace/name`) or a **local directory
containing `config.json`** — which is the `pretrained_model/` subdirectory, not the
step directory above it. There is no `--policy.subfolder` flag.

### Two things differ from the cup-stacking model — check both

**Resolution is 640×360, not 640×480.** The source demonstrations were recorded at
1920×1080; they were downscaled to 640×360 for training, preserving the 16:9 aspect
ratio. Your cameras must be configured to match.

**The cameras are `front` and `ee`**, not `front` and `gripper`:

| Key | Shape |
|---|---|
| `observation.images.front` | `(3, 360, 640)` |
| `observation.images.ee` | `(3, 360, 640)` |
| `observation.state` | `(6,)` |

Joint order is `shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper`,
in degrees. Swapping the two cameras produces confident but wrong behaviour rather than
an error.

To try the speed-up, raise `--policy.speed_up` to 2.0 or 4.0. The paper reaches 4× on
cube picking specifically, so this task should tolerate it better than precise-placement
tasks — but validate at 1× first, and note this policy has not been shown to complete
the task at any speed.

### Requirements

This policy type is not in upstream LeRobot. You need a checkout containing the
`bspline_act` policy, installed with the scipy extra:

```bash
uv pip install -e ".[feetech,bspline]"
```

`scipy>=1.15` is required for `interpolate.generate_knots`.

Unnormalization statistics are stored as buffers inside `model.safetensors`, so each
checkpoint is self-contained — you do not need the training dataset on the robot machine.

## Checkpoints

| Step | Epoch | Train error | **Held-out error** | Eval loss | |
|---|---|---|---|---|---|
| **10000** | 58 | 6.19° | **22.81°** | **0.3903** | **recommended — best** |
| 20000 | 116 | 4.67° | 24.15° | 0.3986 | |
| 30000 | 174 | 3.90° | 24.77° | 0.3996 | |
| 40000 | 234 | 3.44° | 26.22° | 0.4030 | final, worst held-out |

Held-out error gets **monotonically worse** after step 10,000 while training error keeps
falling. Use 10000. Later checkpoints are included only for reproducibility — they are
strictly worse policies.

Error is mean absolute joint error in degrees, worst joint, over each predicted
segment's horizon (~0.76 s), measured by decoding the spline and comparing against
ground-truth actions.

## Training

| | |
|---|---|
| Steps | 40,000 |
| Batch size | 32 |
| Epochs | 234 |
| Dataset | [`atharva-pantheon/pick_block_bowl`](https://huggingface.co/datasets/atharva-pantheon/pick_block_bowl) → downscaled → idle-trimmed → B-spline converted |
| Episodes | **13 train / 3 held out** |
| Frames | 6,422 (from 10,342; 37.9% boundary idle removed) |
| Optimizer | AdamW, lr 2e-5 (backbone 1e-5), wd 1e-3 |
| Precision | bf16 autocast + `channels_last` |
| Augmentation | ColorJitter (brightness/contrast/saturation/hue/sharpness), up to 3 per frame |
| Hardware | 1× RTX 4090, ~2 h 45 min total |
| Seed | 1000 |

Augmentation and 10× weight decay were added specifically to combat overfitting. They
slowed the fit to training data and **moved held-out loss by 0.0003**. An unaugmented
control run is available on request; the two are indistinguishable on unseen data.

### Preprocessing

Source video is 1920×1080 AV1, which is unusable for ACT directly — a ResNet18
stride-32 feature map is 34×60, putting over 4,000 tokens into the transformer encoder
across two cameras. Transcoded to 640×360 H.264, which also sped up random-access
decode from 40 to 183 frames/s per worker.

Leading and trailing motionless frames were then removed: 46 s of lead-in and 27 s of
trailing idle. This dataset is unusually idle-heavy — 37.9% of frames — including
20 s and 32 s dead stretches at the ends of two episodes where recording continued
after the task finished.

Motion is measured as peak-to-peak joint range over a ±0.5 s window rather than
frame-to-frame delta, because isolated jitter samples inside a dead stretch defeat a
delta test and silently leave the idle in.

## Architecture

Standard ACT with one change: the decoder emits a B-spline **parameter matrix** rather
than an action chunk.

```
(n_knots, 1 + action_dim) = (16, 7) = 112 values per prediction
   column 0   knot vector, in source-frame units, 0 = "now"
   columns 1: control points, one per joint
```

| | |
|---|---|
| Params | 51.6 M |
| Vision backbone | ResNet18 (ImageNet init, fine-tuned) |
| dim_model / chunk_size | 512 / 16 |
| VAE | enabled, kl_weight 10.0 |
| B-spline degree | 3 (cubic, C² continuous) |
| bspline_chunk_size | 10 |
| Fitting tolerance ε | 0.2 (degrees) |
| Max knot span | 6 frames |

The paper uses a DINOv2 encoder for its regression variant; this port uses LeRobot's
ResNet18, which is what LeRobot's ACT supports. The paper reports no backbone ablation,
and holds the encoder fixed between its baseline and its B-spline variant, so none of
its claims depend on that choice.

Segments span 0.50–1.10 s (0.76 s mean). The network runs only when a segment is
exhausted, which decouples policy rate from control rate.

### On `max_knot_span`

This dataset is why the flag exists. Adaptive knot fitting covers a motionless stretch
with one enormous knot span, and uncapped fitting here produced segments with a **p95
of 18.4 seconds** — the policy would commit to eighteen seconds of open-loop motion
without looking at a camera. Capping span width at 6 frames bounds that at
`chunk_size × 6` = 2 s, costing 0.04× compression.

After correct idle-trimming the uncapped p95 drops to 1.17 s on its own, so the dead
recording time was the real cause. The cap remains as a cheap safety rail.

## Evaluation

**Offline only. No hardware evaluation has been performed.**

| | Train (13 eps) | **Held-out (3 eps)** |
|---|---|---|
| Best checkpoint (10k) | 6.19° | **22.81°** |
| Final (40k) | 3.44° | 26.22° |

Reference points:

- **Inter-demonstration spread: 54.83°** — how far apart two human demonstrations of
  this task are, phase-aligned. At 22.8° the policy is well inside that, so it is
  extracting real per-episode information from the cameras rather than replaying an
  average trajectory. This is the one genuinely positive result.
- **Hold-still baseline: 16.23°.** The policy scores worse than this. Over a 0.76 s
  horizon on a slow task, "don't move" is a decent short-horizon *predictor* while
  being a useless *policy*, so this is not the damning comparison it looks like — but
  combined with the widening train/eval gap it is not encouraging either.

Held-out loss plateaued at ~0.39 by step 2,000 and never improved across the remaining
38,000 steps.

### Why the held-out number is a pessimistic bound

The split holds out the last 3 of 16 episodes. Inspecting the raw recording:

- 8 of 15 episode boundaries are contiguous to under 1° (several to exactly 0.00°), so
  episodes were largely cut from a continuous teleoperation stream. Adjacent episodes
  are correlated, meaning an interleaved split would leak.
- There is a 97.67° discontinuity between episodes 9 and 10, and episodes 10, 13, 14
  and 15 all end with the gripper open (8–16°) while the other twelve end closed (~2°).
- **All three held-out episodes sit after that change point**, and two of them *start*
  with the gripper open — a state the model never sees at t=0 in training.

So the tail split is biased pessimistic and an interleaved split would be biased
optimistic. With 16 semi-continuous episodes there is no clean holdout. 22.8° is a
lower bound on quality, not a verdict.

## Limitations

- **13 training episodes is too few for this task.** This is the dominant limitation and
  no hyperparameter fixed it. 40–50 demonstrations with varied cube placement and an
  **independent reset between takes** would address both the training and the
  measurement problem.
- **No hardware evaluation.** Task success rate is unknown, and given the above it
  should not be assumed to be non-zero.
- **Held-out split is confounded** — see above.
- **Resolution and camera names differ** from the cup-stacking model. Check both.
- **Camera assignment is silent when wrong.**

## Citation

```bibtex
@article{han2026b,
  title={B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations},
  author={Han, Xiaoshen and Xiong, Haoyu and Chen, Haonan and Liu, Chaoqi and
          Torralba, Antonio and Zhu, Yuke and Du, Yilun},
  journal={arXiv preprint arXiv:2607.09648},
  year={2026}
}
```