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observation.state
list
action
list
timestamp
float32
frame_index
int64
episode_index
int64
index
int64
task_index
int64
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1.52
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1.7
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[ 0.021299004554748535, 0.10079124569892883, 0.1130739375948906, 0.16434156894683838, 0.023696184158325195, -0.11958903819322586, 0.06844529509544373, -0.12991538643836975, -0.18798460066318512, 0.14363819360733032, 0.1172943115234375, 0.11548316478729248, -0.07820644974708557, 0.07688964903...
1.72
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[ -0.08002099394798279, 0.012386075221002102, 0.7868950366973877, 0.9997203350067139, 0.0003182378422934562, -0.022169947624206543, -0.008227594196796417, -0.05896007642149925, 0.11749739199876785, 0.1057281568646431, 0.16671226918697357, -0.05168477073311806, -0.08508611470460892, 0.0077200...
[ 0.02303507924079895, 0.1011769101023674, 0.11395537853240967, 0.1675381064414978, 0.03011864423751831, -0.12028505653142929, 0.07344144582748413, -0.1284618228673935, -0.1861914098262787, 0.14935463666915894, 0.12901321053504944, 0.11442691087722778, -0.07696712017059326, 0.074742630124092...
1.74
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1.82
91
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1.84
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1.86
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1.88
94
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1.9
95
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95
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End of preview.

Kimodo → Unitree G1 Motion Dataset (v1)

10,000 episodes of text-conditioned humanoid motion for the Unitree G1, generated by streaming nvidia/Kimodo-G1-RP-v1 diffusion into a MuJoCo physics rollout via strands-labs/robots.

  • Robot: Unitree G1 (29 DoF, 65-dim proprioceptive state)
  • Format: LeRobot v3.0 (parquet)
  • Total frames: 1,990,000
  • Total episodes: 10,000
  • Episode length: 199 frames (4.0 s @ 50 Hz)
  • Control frequency: 50 Hz
  • Physics: MuJoCo (1 kHz sim, 50 Hz control)
  • Motion source: Kimodo text-to-motion diffusion (kinematic prior)
  • Tracker: ProtoMotions GTP (BeyondMimic) ONNX PD policy
  • Task prompts: 25 base motion categories × ~400 stylistic variants ≈ 10 k unique prompts

Task categories (25)

idx task
0 perform: a person raising both arms overhead
1 perform: a person bowing forward
2 perform: a person throwing boxing punches
3 perform: a person clapping hands with enthusiasm
4 perform: a person crouching down low
5 perform: a person dancing to music with rhythm
6 perform: a person jumping up with both hands raised
7 perform: a person performing a front kick
8 perform: a person sitting cross-legged in a meditation pose
9 perform: a person crouching down to pick up an object
10 perform: a person pulling something toward the body
11 perform: a person pushing a heavy object forward
12 perform: a person reaching forward with both hands
13 perform: a person running forward at moderate pace
14 perform: a person raising the right hand in a salute
15 perform: a person stepping sideways
16 perform: a person doing bodyweight squats
17 perform: a person performing bodyweight squats
18 perform: a person swaying gently side to side
19 perform: a person standing in a T-pose with arms out
20 perform: a person turning around in place
21 perform: a person twisting the torso left and right
22 perform: a person walking forward with confident strides
23 perform: a person walking backward carefully
24 perform: a person waving hello with the right hand

Episodes are approximately evenly distributed across the 25 tasks (~400 episodes/task).

Schema

Each row is one 50 Hz control step:

feature dtype shape description
observation.state float32 (65,) G1 proprioceptive state (root pos/quat/lin+ang vel + 29 joint pos + 29 vel)
action float32 (29,) 29-DoF PD joint-position targets (normalized)
timestamp float32 (1,) seconds from episode start
frame_index int64 (1,) step within episode (0..198)
episode_index int64 (1,) global episode id (0..9999)
task_index int64 (1,) task category id (0..24), maps to meta/tasks.parquet
index int64 (1,) global frame id

Sample row

observation.state[:5] = [-0.0114, -0.0054,  0.7768,  0.9999, -0.0112]
                        #  root_x   root_y  root_z   quat_w   quat_x ...

action[:5]            = [ 0.0991,  0.1640,  0.1117,  0.1538,  0.1554]
                        #  left_hip_pitch  right_hip_pitch  ...

Loading

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("cagataydev/kimodo-g1-motion-v1")
print(ds)                       # 1_990_000 frames, 10_000 episodes
sample = ds[0]                  # {"observation.state": ..., "action": ..., "task": "..."}

# By episode
ep = ds.hf_dataset.filter(lambda x: x["episode_index"] == 42)

# By task category
walk_forward = ds.hf_dataset.filter(lambda x: x["task_index"] == 22)

Streaming works out of the box:

from datasets import load_dataset
ds = load_dataset(
    "cagataydev/kimodo-g1-motion-v1",
    streaming=True, split="train",
)
for sample in ds.take(10):
    print(sample["episode_index"], sample["frame_index"])

Generation pipeline

text prompt
   │
   ▼
Kimodo diffusion (nvidia/Kimodo-G1-RP-v1)
   │   • 100 denoising steps, 30 Hz kinematic motion
   ▼
SLERP upsample 30 Hz → 50 Hz  (120 → 199 frames)
   │
   ▼
ProtoMotions GTP (BeyondMimic tracker, ONNX)
   │   • text-conditioned kinematic prior → PD joint targets
   ▼
MuJoCo physics rollout (1 kHz sim, 50 Hz control)
   │
   ▼
LeRobot v3 parquet shards (streamed to HF)

Per-episode timings on Thor (Jetson AGX Thor Dev Kit, 128 GB):

  • Kimodo motion gen: ~8 s
  • ProtoMotions FK conversion (cached after first): ~1 s (38 s first-run text-encoder init)
  • Physics sim + record: ~1.2 s (3.2× realtime)
  • Total: ~10 s/episode, or ~28 h wall for 10 k episodes.

Prewarmed qpos caches for all 25 base motions live in qpos_cache/ in the generator repo (≈ 5 MB total). This is what enables the streaming pipeline to hit ~10 s/episode.

Reproducibility

  • Motion prior: nvidia/Kimodo-G1-RP-v1
  • Tracker: ProtoMotions GTP unified_pipeline.onnx (uploaded to cagataydev/protomotions-gtp-g1)
  • Robot model: Unitree G1 MJCF from strands-labs/robots
  • Generator code: internal @ cagataycali/robots fork, branch feat/kimodo-mujoco-integration (PR ↔ strands-labs/robots pending)
  • Prompt schema: BASE_MOTIONS (25) × STYLES (20) × SUBJECTS (20) random combinations

Intended use

  • Training VLA / motion-tracking / imitation policies on the G1
  • Pretraining kinematic priors for BeyondMimic-style RL loops
  • Benchmarking text-to-motion → physics tracking pipelines
  • Cross-embodiment motion transfer research

Limitations

  • Motion is kinematic-first: physics tracking succeeds on ~99 % of episodes but a small tail of extreme prompts (rapid kicks, jumps) can produce mild foot-slip or root drift. No per-frame contact / balance labels included in v1.
  • All episodes are 4.0 s (199 frames). Longer horizons will land in v2.
  • No camera / RGB observations — proprioception + task text only.

Citation

@dataset{kimodo_g1_motion_v1_2026,
  author  = {Cagatay Cali},
  title   = {Kimodo → Unitree G1 Motion Dataset v1},
  year    = {2026},
  url     = {https://huggingface.co/datasets/cagataydev/kimodo-g1-motion-v1},
  note    = {10k text-conditioned humanoid episodes, LeRobot v3 format,
             generated via Kimodo diffusion + ProtoMotions GTP + MuJoCo}
}

Please also cite:

  • Kimodo: nvidia/Kimodo-G1-RP-v1
  • ProtoMotions / BeyondMimic: NVIDIA GEAR Lab
  • LeRobot: Cadene et al., HuggingFace
  • strands-labs/robots: agent-first robotics framework

License

Apache-2.0 for the dataset. Underlying models (Kimodo, ProtoMotions) retain their own licenses.

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