---
license: mit
task_categories:
- robotics
tags:
- LeRobot
- GR00T
- GR00T-N1.7
- humanoid
- unitree-g1
- inspire-hand
- tactile
- effort
- depth
- manipulation
- laboratory
pretty_name: G1 + Inspire — eject the pipette tip into the rack (GR00T N1.7, G1_INSPIRE)
---

# G1 + Inspire — "eject the pipette tip into the rack" (GR00T N1.7 / `G1_INSPIRE`)

Teleoperated **Unitree G1 (29-DoF) + Inspire RH56DFTP hands** manipulation data,
converted to the **GR00T-flavored LeRobot v2.1** format for fine-tuning GR00T N1.7
as a custom `NEW_EMBODIMENT` (here called `G1_INSPIRE`).

Same schema as
[`MLeggiero/g1-gr00t-inspire-pick_and_place`](https://huggingface.co/datasets/MLeggiero/g1-gr00t-inspire-pick_and_place),
with two additions: **`observation.effort`** (per-joint torque) and **native
1280×720** ego-view video and depth instead of 424×240. Read
[Known limitations](#known-limitations) before training on this — the hand
proprioception channel is not usable.

## Summary
- **Task (1):** `eject the pipette tip into the rack`
- **Episodes / frames:** 68 / 61,609 @ 60 fps; `success` flag per episode in `meta/episodes.jsonl`
- **Sessions:** 3 capture sessions on 2026-08-03 (`20260803_1445`, `20260803_1455`, `20260803_1511`)

  | task_index | task | episodes | success / fail |
  |---|---|---|---|
  | 0 | eject the pipette tip into the rack | 68 | 68 / 0 |

- **Camera:** onboard Intel RealSense **D435** ego-view (`observation.images.ego_view`, **1280×720**, h264)
- **observation.state:** 63-dim · **action:** 30-dim · **observation.tactile:** 34-dim · **observation.effort:** 29-dim

### The task
The robot grips a micropipette in its right hand, moves it over a 96-well tip
rack, and presses the tip ejector with its thumb to release the tip into the
rack. The four fingers stay clamped on the pipette barrel throughout; the thumb
is the only actuated hand DoF.

## Data fields & vector index

### `observation.state` (float32[63]) — proprioception (+ pooled tactile)
| idx | group | per-element (in order) | unit |
|----|-------|------------------------|------|
| 0–6   | `left_arm`  | shoulder_pitch, shoulder_roll, shoulder_yaw, elbow, wrist_roll, wrist_pitch, wrist_yaw | rad (absolute joint angle) |
| 7–13  | `right_arm` | shoulder_pitch, shoulder_roll, shoulder_yaw, elbow, wrist_roll, wrist_pitch, wrist_yaw | rad |
| 14–19 | `left_hand` | pinky, ring, middle, index, thumb_pitch, thumb_yaw | rad (Inspire RH56DFTP) — **constant, see limitations** |
| 20–25 | `right_hand`| pinky, ring, middle, index, thumb_pitch, thumb_yaw | rad — **constant, see limitations** |
| 26–28 | `waist`     | yaw, roll, pitch | rad |
| 29–45 | `tactile_left`  | 17 hand regions (see region list) | log1p(pooled) |
| 46–62 | `tactile_right` | 17 hand regions | log1p(pooled) |

The **baseline** `meta/modality.json` uses only `0:29` (tactile-free); the **tactile** `meta/modality.tactile.json` uses `0:63`.

### `action` (float32[30]) — GR00T prediction target
| idx | group | per-element (in order) | unit |
|----|-------|------------------------|------|
| 0–6   | `left_arm`  | shoulder_pitch … wrist_yaw (7) | rad |
| 7–13  | `right_arm` | shoulder_pitch … wrist_yaw (7) | rad |
| 14–19 | `left_hand` | pinky, ring, middle, index, thumb_pitch, thumb_yaw | rad — **constant** |
| 20–25 | `right_hand`| pinky, ring, middle, index, thumb_pitch, thumb_yaw | rad — **only thumb_pitch/thumb_yaw vary** |
| 26    | `base_height` | commanded base height | m |
| 27    | `navigate_command[0]` = vx | base linear velocity, local frame | m/s |
| 28    | `navigate_command[1]` = vy | base linear velocity, local frame | m/s |
| 29    | `navigate_command[2]` = yaw_rate | base yaw rate | rad/s |

Arm/hand action targets are stored as **absolute** joint angles (radians), matching the upstream
`g1-gr00t-inspire-pick_and_place` dataset. Lower body: GR00T does **not** output leg
joints — it emits a base velocity (`navigate_command`) + `base_height`, which the WBC turns into
leg motion.

### `observation.effort` (float32[29]) — per-joint torque
Estimated joint torque (`dof_tau`) in **N·m**, in the canonical 29-joint order
(`left_leg` 0–5, `right_leg` 6–11, `waist` 12–14, `left_arm` 15–21, `right_arm` 22–28) —
i.e. the full-body joint order, *not* the arm-first ordering used by `state`/`action`.
Not referenced by either modality config; use it as an auxiliary supervision or
contact-inference signal. All 29 dims are live.

### `observation.tactile` (float32[34]) — pooled tactile, also embedded in `state[29:63]`
Per hand, 17 regions in this order (per-region **max-pool** of the raw taxels, then `log1p`):
`little_tip, little_nail, little_pad, ring_tip, ring_nail, ring_pad, middle_tip, middle_nail, middle_pad, index_tip, index_nail, index_pad, thumb_tip, thumb_nail, thumb_middle, thumb_pad, palm`
→ dims `0–16` = left hand, `17–33` = right hand.

### Other parquet columns
| column | dtype | meaning |
|--------|-------|---------|
| `timestamp` | float32 | seconds from episode start (`frame_index / 60`) |
| `frame_index` | int64 | 0…T−1 within the episode |
| `episode_index` | int64 | 0…67 |
| `index` | int64 | global frame index, 0…61608 |
| `task_index` | int64 | always 0 (single task) |
| `annotation.human.task_description` | int64 | task id → text via `meta/tasks.jsonl` |

### `meta/`
- `info.json` — schema (codebase_version v2.1, fps 60, robot_type unitree_g1, per-feature shapes/names).
- `episodes.jsonl` — per episode: `episode_index, tasks, length, success (bool), source` (original `<date>_<time>/episode_NNNN` session).
- `tasks.jsonl` — the single task string.
- `stats.json` — per-feature mean/std/min/max/q01/q99 (GR00T normalization), including `observation.effort`.
- `modality.json` (baseline) / `modality.tactile.json` (tactile) — GR00T modality slices.

### Sidecars (raw, not referenced by the modality configs)
- `tactile/episode_0000NN.npz` → `left`, `right`: **(T, 1062) uint16** raw taxels (0…~65535). The 1062 split into the 17 regions above with grid shapes:
  tip 3×3 (9), nail 12×8 (96), pad 10×8 (80) for little/ring/middle/index; thumb = tip 3×3 (9) + nail 12×8 (96) + middle 3×3 (9) + pad 12×8 (96); palm 8×14 (112). (Σ = 1062.)
- `depth/episode_0000NN.npz` → `depth`: **(T, 720, 1280) uint16**, depth in **millimeters**, spatially registered to the ego-view frames.

## Units & conventions
Arm/waist/hand angles in **radians**; hands are Inspire RH56DFTP DoFs mapped from raw 0–1000
registers to radians via the joint limits. `base_height` in meters, `navigate_command` in
m/s & rad/s (robot-local frame). Effort in **N·m**. Tactile in `observation.*` is `log1p` of
per-region max-pooled raw taxels; the `tactile/` sidecar holds the un-pooled uint16 taxels.

## Known limitations

These are properties of the capture, not of the conversion. Stated plainly so you
can decide what to train on.

1. **Hand proprioception is dead.** `state[14:26]` (all 12 hand dims, both hands) is
   **constant for every frame of every episode**. The recorder's hand-state stream was
   not updating during capture — its source timestamp is frozen and ~70 h stale. Do
   not use hand state as an input feature; mask it or use `modality.json`'s `0:29`
   slice with the hand block excluded.
2. **Action hand channel is nearly constant.** `action[20:26]` (right hand) varies in
   only **2 of 6 dims** (`thumb_pitch`, `thumb_yaw`); the four fingers are pinned
   closed on the pipette barrel. `action[14:20]` (left hand) is fully constant — the
   left hand is unused and held open.
3. **Single-arm task.** Only the right arm manipulates. Left-arm state/action are
   present and do vary slightly (postural), but carry no task signal.
4. **All episodes are labelled `success: true`.** The capture contains no negative
   examples, so `success` carries no discriminative information here.
5. **One very short episode** (~119 frames ≈ 2 s) is present and is likely an aborted
   take; filter on `length` in `meta/episodes.jsonl` if that matters.
6. **Body state, effort, tactile and vision are all healthy** — `state[0:14]`,
   `state[26:29]`, all 29 effort dims, both tactile blocks and the ego-view video vary
   normally. The usable learning signal is arm + waist + tactile + effort + vision.

## Fine-tuning (GR00T N1.7)
```bash
python gr00t/experiment/launch_finetune.py \
  --base-model-path nvidia/GR00T-N1.7-3B \
  --dataset-path <this dataset> \
  --embodiment-tag NEW_EMBODIMENT \
  --modality-config-path g1_inspire_modality_config.py \
  --num-gpus 1 --global-batch-size 4
```

Note that `observation.images.ego_view` here is 1280×720, not the 424×240 of
`g1-gr00t-inspire-pick_and_place`. GR00T resizes internally, but the two datasets are
**not** byte-compatible; co-training requires a resize step.

## Provenance & license
**Author:** MLeggiero. Data collected on a Unitree G1 + Inspire RH56DFTP teleoperation rig,
using the open-source collection stack from the YanjieZe repository (tooling credit only).
Captured 2026-08-03. Released under the **MIT License**.