license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so101
- bin-sorting
- vla
- language-conditioned
configs:
- config_name: default
data_files: data/chunk-*/file-*.parquet
naive-bench — Bin Sorting
A language-conditioned bin-sorting teleoperation dataset on a single SO-101 arm, collected for Project 2: Naive Bench — a hobbyist-oriented benchmark for comparing robot manipulation policies (ACT, VLAs, …) under fixed compute/data constraints.
The task is deliberately designed to differentiate VLAs from ACT/WAMs: the bins are arbitrary named targets (there is no color-matching rule), so the only way to route a bar correctly is to read both slots of the prompt — the bar color and the bin color. A policy that ignores the instruction cannot succeed, which makes the eval score a direct measure of language grounding.
Task
put the {bar_color} bar into the {bin_color} bin
- Bins (4): white, yellow, orange, blue — fixed positions, arbitrary targets (no color-matching rule).
- Bar colors (6): blue, purple, orange, red, yellow, green (~9 physical bars per color).
- Instruction space: 6 bars × 4 bins = 24 possible commands.
Every training scene contains a single bar. The target bin is varied across each bar's
episodes so a policy cannot ignore the {bin_color} slot and still succeed.
Dataset summary
| Robot | SO-101 follower (so101_follower), 6 position-controlled joints |
| Cameras | arm_camera + overhead_camera, 240×320 RGB, AV1 |
| Control / video rate | 30 fps |
| Episodes | 312 |
| Frames | 115,378 |
| Tasks (trained commands) | 20 (of the 24-command space; 4 held out) |
| Format | LeRobotDataset v3.0 |
State / action are the six SO-101 joint positions, in this order:
shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos.
This ordering is load-bearing — it matches the follower's native motor order, not
alphabetical.
Held-out (bar, bin) pairs
Four (bar, bin) combinations are deliberately kept out of training so the benchmark's held-out tiers test composition, not memorization:
- blue bar → orange bin
- red bar → white bin
- yellow bar → blue bin
- green bar → orange bin
The other 20 pairs appear in training and are the 20 tasks in meta/tasks.parquet.
(purple and orange bars are fully trained across all four bins.)
Recording distribution
Per-cell episode counts. ✗ = held out (nothing recorded). A bar with one held-out bin spreads its episodes across three bins; a fully-trained bar spreads across four.
| Bar ↓ / Bin → | white | yellow | orange | blue | total |
|---|---|---|---|---|---|
| blue | 18 | 18 | ✗ | 18 | 54 |
| red | ✗ | 18 | 18 | 18 | 54 |
| yellow | 18 | 18 | 18 | ✗ | 54 |
| green | 18 | 18 | ✗ | 18 | 54 |
| purple | 12 | 12 | 12 | 12 | 48 |
| orange | 12 | 12 | 12 | 12 | 48 |
| total | 78 | 96 | 60 | 78 | 312 |
Within each cell, bar start position and physical bar instance (~9 per color) are randomized so the policy doesn't overfit a single bar or spot. Bin totals are uneven because two bars hold out the orange bin — harmless for training a per-command policy.
Evaluation protocol
The dataset is scored with a graded, multi-tier rollout protocol (defined in the Naive Bench "Bin Sorting Evals" design). Bins are fixed; bar start positions are uniformly randomized; colors are counterbalanced ~uniformly within each tier; distractor bars in multi-bar tiers are other trained colors.
| Tier | Scene | Command | Rollouts | Primarily tests |
|---|---|---|---|---|
| T0 — Sanity | 1 bar | seen (bar, bin) | 10 | Reproduces training dist. (diagnostic floor) |
| T1 — Held-out routing | 1 bar | held-out (bar, bin) | 30 | Compositional grounding — the two slots learned independently |
| T2 — Two-bar grounding | 2 bars | name one, seen pairing | 30 | Object disambiguation by language (headline number) |
| T3 — Three-bar grounding | 3 bars | name one | 20 | Grounding under heavier clutter |
| T4 — Two-bar + held-out | 2 bars | named bar → held-out bin | 20 | Hardest: disambiguate object and route to unseen target |
Total: 110 rollouts per policy.
Scoring (per episode, exactly one bar named)
| Outcome | Credit |
|---|---|
| Named bar placed in named bin | +1.0 |
| Named bar placed in wrong bin (right object, failed routing) | +0.5 |
| Named bar grasped + lifted, not placed | +0.3 |
| Named bar contacted, grasp failed | +0.1 |
| No meaningful interaction with named bar | 0 |
| Grasps/moves a non-named bar (selective-attention failure) | −0.5 |
Episode score ∈ [−0.5, 1.0]. The two partial-credit paths (right object / wrong bin = +0.5 vs wrong object = −0.5/0) separate a bin-grounding failure from a bar-grounding failure. Report mean ± CI per tier; if a scalar is needed, weight toward grounding and drop T0:
grounding_score = 0.30·T1 + 0.35·T2 + 0.20·T3 + 0.15·T4
At low n (10–30 rollouts), use paired scenes (same randomized layouts across all policies within a tier), graded scoring, and report CIs rather than bare point estimates.
Loading
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("binhpham/naive-bench")
print(ds.meta.total_episodes, ds.meta.total_frames)
sample = ds[0] # observation.state, observation.images.*, action, task, ...
Collection
Recorded with the naive-bench runtime — a
LiveKit Portal room where a human teleoperates the SO-101 leader arm and a HITL recorder
pairs each executed action with the observation it was responding to. See the repo's
operators/teleoperator/ for the recorder.