Sim rollouts

A trained policy is a neural network — nothing to watch. What's worth seeing is the policy acting: the agent walking, reaching, flying. This guide turns one rollout into a single MCAP DreamLake plays natively — the mesh moving through the scene, every reward and joint a cursor-synced chart. No screen recording, no bespoke viewer.

Anything with a body and a 3D pose fits — robot, character, drone, hand. DreamLake already ships the last two moves; this guide is mostly the first.

Prerequisites

pip install foxglove-sdk trimesh          # write MCAP; export meshes to OBJ
curl -fsSL https://dl.dreamlake.ai/install.sh | bash   # the dreamlake CLI
dreamlake login                           # browser sign-in

Generate the MCAP

Roll the policy out and log three channels, each a Foxglove well-known schema DreamLake decodes with zero config.

import foxglove
from foxglove.messages import FrameTransforms, SceneUpdate

w = foxglove.open_mcap("rollout.mcap")
obs, _ = env.reset()
for t in range(STEPS):
    obs, reward, done, _ = env.step(policy(obs))
    ns = int(t * step_dt * 1e9)
    foxglove.log("/tf", FrameTransforms(transforms=body_transforms(env)), log_time=ns)
    if t == 0:
        foxglove.log("/robot", SceneUpdate(entities=robot_meshes(env)), log_time=ns)  # once
    foxglove.log("/metrics", {"reward": float(reward), **joint_angles(env)}, log_time=ns)
w.close()

The skill ships a runnable template that fills the two helpers above and gets the easy-to-miss details right — quaternion order (wxyz → xyzw) and exporting meshes in a format the viewer reads today (OBJ or GLB).

Connect a source

The .mcap is data like any other — DreamLake reads it in place, never imports. Put it in storage you already reach, then link it on the namespace's Sources page (full rules):

hf upload your-name/your-repo ./rollout.mcap --repo-type dataset   # or S3 / Dropbox

Watch it

A .dreamrc beside the file names which channel feeds which view — embedded mesh, a motion trail, the metric charts:

version: 1
dataset:
  format: mcap
  episodes: auto
views:
  - view: recon3d
    up: z
    tracks: [{ field: "/tf::*", as: transform3d }]
    geometry: ["/robot::*"]              # the embedded OBJ meshes
    trail: { ahead: 1, behind: 0.5 }
  - view: timeline
  - view: lineChart
    title: "/metrics"
    series: ["/metrics"]

Name it .dreamrc for a folder, or <file>.mcap.dreamrc for one mcap when the folder holds several. Keys, options, and the validate loop: the Dataset Viz docs.

Bring your own robot

Two ways to put a body on the skeleton — pick one.

Embed the mesh for a self-contained file, or — if DreamLake's robot registry (live9080/dreamlake-robots) has your robot and its link names match your /tf frames — emit a /tf-only MCAP and let the .dreamrc load the URDF.

Let Claude do it

The sim-to-mcap skill runs this end to end and hands off to the source and dataset-viz skills:

Next steps