Record a running CARLA simulation to a .log file for later replay or inspection. Capture object creation, movement, and traffic-light states, optionally include controls and velocities, and verify that the recording contains simulation frames.
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Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.
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Paths.
scripts/…andreferences/…below are relative to the directory holding this SKILL.md. Your working directory is the user's project, not that directory, so prefix them with its absolute path or the command is not found.
Capture a running simulation to a .log you can replay later (replay-recording)
or interrogate (query-recording). The deliverable
is a verified log: after stopping, the file is parsed back from the server to
confirm it holds frames — start/stop alone confirm nothing.
What it captures: world state per frame — actor create/destroy, transforms,
traffic-light states, vehicle wheel/animation and walker bones. With --extra
(additional_data) also velocities, accelerations and control inputs. Not
captured: sensor output (camera/lidar) — those re-simulate only on replay.
The .log is written on the server's filesystem (see the reference for
exactly where).
Progress:
- [ ] Step 1: Check prerequisites (bash scripts/check_env.sh), clear FAILs
- [ ] Step 2: Start recording (or use `clip` for a fixed duration)
- [ ] Step 3: Let the simulation run (traffic, scenario, manual drive, ...)
- [ ] Step 4: Stop, then verify the log parses back with frames
Commands need CARLA_HOST/CARLA_PORT from scripts/env.sh. Prefix with
source scripts/env.sh or export them.
bash scripts/check_env.sh
source scripts/env.sh
# manual bracketing: start, do things, stop
python3 scripts/record.py start --file /tmp/run.log
# ... run traffic / drive / trigger a scenario ...
python3 scripts/record.py stop
# or record a fixed-length clip and auto-verify
python3 scripts/record.py clip --file /tmp/run.log --seconds 20
# capture velocities/controls too (larger file)
python3 scripts/record.py start --file /tmp/run.log --extra
Use an absolute --file so you know where the log lands (a relative name
goes under the server's CarlaUE4/Saved/).
clip verifies automatically; after a manual stop, confirm with the
query-recording skill:
python3 ../query-recording/scripts/query.py info --file /tmp/run.log
A valid log shows a frame count and a duration. Zero frames means nothing ran between start and stop, or the path was wrong.
Example 1: capture a traffic run
User says: "record 30 seconds of traffic"
Start traffic (traffic-manager skill), then clip --file /tmp/traffic.log --seconds 30. VERIFY shows frames + duration ~30s.
Example 2: record around a manual action
User says: "record while I run my scenario"
start --file /tmp/scenario.log, run the scenario, stop, then query info.
Problem: log has 0 frames / query says empty Cause: nothing simulated between start and stop, or a bad server-side path. Solution: ensure actors are moving; use an absolute writable server path.
Problem: replayed run has no camera images
Cause: the recorder never stores sensor data.
Solution: expected — re-attach sensors and replay with --replay-sensors
(replay-recording skill) to regenerate them.
Problem: file not found where I expected Cause: relative name resolves on the server, under CarlaUE4/Saved/. Solution: pass an absolute path, and remember it is on the server machine.
.log recording on the server (path from --file), verified to contain
frames. Feed it to replay-recording or
query-recording.Detail (capture contents, file location, size) in references/recorder.md.
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Add a message type to CARLA's native ROS 2 interface. Create its C++ data structure, serialization code for transmission, type identity hash, registration, and tests, with Docker-based hash calculation and build checks.
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Extend CARLA’s native ROS 2 interface to publish sensor data or receive vehicle commands. Covers topic implementation, registration, data connections, and verification, plus adding middleware, the communication layer used to exchange messages.
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Summarize ScenarioRunner results to see which scenarios passed and which checks failed. Compare runs before and after changes, or analyze recorded driving to produce measurements and plots of vehicle distance and deviation from the lane center.
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Checks a local CARLA checkout for missing API documentation, outdated references, broken links, and pages absent from navigation. Produces a grouped Markdown report without building the project or running the simulator.
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Read and display CARLA object bounding boxes, then project them onto a camera image as 2D rectangles. Export an annotated PNG and box coordinates in JSON for object detection annotation work.
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Builds CARLA's ue4-dev branch on Linux, producing the Unreal Engine 4.26 editor, server modules, and an installed Python client package. Fetches required content, optionally compiles native ROS 2 support, and verifies the build against a source-built server.
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