DreamDojo 2B · AGIBOT

VariantsAGIBOTGR-1 G1 YAM

2B-parameter action-conditioned video world model for the AgiBot G1 humanoid.

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Example usage

DreamDojo 2B · AGIBOT runs on the Dream Engine inference stack and is accessible via the typed Python SDK. Generate an API key, set it as DREAM_API_KEY in your shell, and you’re ready to roll out.

Input

PYTHON

1# Install: pip install dream-engine
2import dream
3import numpy as np
4from PIL import Image
5 
6client = dream.Client()  # reads DREAM_API_KEY
7 
8# Roll out 49 frames of 480x640 video. ~$0.0245 per call.
9img = Image.open("start.png")
10acts = np.load("actions_agibot.npy")  # shape (48, 384)
11 
12rollout = client.models.get("dreamdojo-2b-agibot").predict(
13    start_frame=img,
14    actions=acts,
15    seed=0,
16)
17 
18print("frames:", rollout.frames, "cost:", rollout.cost_usd)
19print(rollout.to_json())  # full server-side envelope
20rollout.save("out.mp4")

JSON output

JSON

1{
2  "id": "rollout_8456fe51db3548789f199cfb8c8efd35",
3  "object": "rollout",
4  "model": "dreamdojo-2b-agibot",
5  "created": 1735236968,
6  "frames": 49,
7  "resolution": [480, 640],
8  "engine_wall_ms": 2616,
9  "cost_usd": 0.0245,
10  "video_url": "https://cdn.dreamengine.com/r/8456fe51.mp4",
11  "video_url_expires_at": 1735323368,
12  "seed": 0
13}

Real-time streaming sessions

For interactive use cases — game-engine inference loops, MPC controllers, agent rollouts where each step’s action depends on the previous frame — open a long-lived session. Frames stream back as they decode (low time-to-first-frame); submit a new action sequence and the last frame threads forward automatically. Read the protocol reference.

Streaming session

PYTHON

1# Install: pip install 'dream-engine[sessions]'
2import dream
3import numpy as np
4from PIL import Image
5 
6client = dream.Client()  # reads DREAM_API_KEY
7 
8# Open a streaming session — frames arrive as they decode (low TTF).
9session = client.sessions.create(
10    model="dreamdojo-2b-gr1",
11    initial_frame=Image.open("start.png"),
12    seed=0,
13)
14try:
15    actions = np.zeros((48, 384), dtype=np.float32)
16    for frame in session.submit_actions(actions):
17        # frame is np.ndarray (H, W, 3) uint8 — display, decode, plan...
18        ...
19    # Submit another action sequence — last frame threads forward
20    # automatically; no re-upload.
21finally:
22    session.close()  # idempotent; refunds unused prebudget

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