Run the Physics Agent CLI for VLM-based physics property classification, tuning, and refine-loop workflows for 3D assets. Use when the user wants to run the physics-agent CLI directly, classify asset components, identify asset type, build datasets from USD files, predict component material/type/physics properties, author simulation-ready USD physics schemas, tune authored physics parameters, or run iterative refine.
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Run the Physics Agent CLI for VLM-based physics property classification, tuning, and refine-loop workflows for 3D assets. Use when the user wants to run the physics-agent CLI directly, classify asset components, identify asset type, build datasets from USD files, predict component material/type/physics properties, author simulation-ready USD physics schemas, tune authored physics parameters, or run iterative refine.
Requires Linux, a Linux container, or WSL2; the physics-agent CLI; a repo Python environment; provider credentials for the selected VLM backend; a render endpoint for remote rendering configs; and a scene optimizer endpoint or local scene optimizer when optimize_usd is enabled.
Physics Agent CLI
The Physics Agent renders USD components, identifies the asset, predicts
material/component/physics properties for each part, and can author UsdPhysics
schemas into a simulation-ready output USD.
When to Use
Use when the user asks to run physics-agent directly from the command line.
Use when the user wants component classification for a USD asset.
Use when the user wants to author rigid-body, collision, mass, and physics
material schemas from VLM predictions.
Use when the user needs the instanced-USD/deinstance path for writable
physics authoring.
Route requests outside the local CLI to the exact sibling skill:
User intent
Required sibling skill
Call an already-running Physics Agent REST API, monitor its sessions, or download service artifacts
physics-agent-client
Build, start, stop, or configure the local Physics Agent Docker Compose service
deploy-physics-agent-docker
Provision Brev-hosted render/VLM dependencies or run the Brev hybrid Physics Agent workflow
deploy-physics-agent-brev
Assign or refine visual/PBR materials rather than physical properties
material-agent-cli
Limitations
Keep secrets out of chat and commits. Tell the user to set provider keys in
their local environment or repo-root .env; never ask them to paste keys.
Official runtime targets are Linux, Linux containers, and WSL2. Native
Windows shell execution is not supported; direct the user to WSL2 or Linux,
not to a sibling skill that does not support the requested runtime.
Config paths such as input.usd_path resolve relative to the config file,
not the current shell directory.
Instanced USD descendants are instance proxies and cannot be authored on
directly. Use optimize_usd with deinstance enabled before apply_physics
when predictions target instance proxies.
restore_usd maps predictions back to original paths for reporting; when
optimization runs, apply_physics authors onto the optimized/deinstanced USD
using raw prediction paths.
The classification pipeline does not apply visual/PBR materials or render a
final beauty image. It stops after physics authoring unless the user invokes
other tooling.
Suspicious scale-driven mass estimates follow the configured
apply_physics.mass_scale_policy; the default skips explicit mass while
still authoring density, collision, and physics material properties.
Prerequisites
Activate the repo Python environment before running commands.
Confirm physics-agent is installed and on PATH.
Set the VLM provider key required by the selected backend. Public defaults
usually use NVIDIA_API_KEY; other supported backends can use
OPENAI_API_KEY, ANTHROPIC_API_KEY, or GOOGLE_API_KEY.
Set RENDER_ENDPOINT and OPTIMIZER_ENDPOINT only when the config uses
remote rendering or optimization services. For a local OVRTX Docker sidecar,
use RENDER_ENDPOINT=http://localhost:8001.
Set WU_S3_BUCKET, WU_S3_PROFILE, WU_S3_REGION, and standard AWS
credentials only when the run uploads assets to S3.
Instructions
Start from the repo root and activate .venv.
Choose a config. For a first local run, use
apps/physics_agent/configs/lightbulb.yaml.
Verify input.usd_path, optimizer settings, render backend, and VLM model.
For instanced assets, enable deinstance under
steps.optimize_usd.scene_optimizer_settings and keep restore_usd
enabled.
Run a dry run before a new or heavily edited config.
Run the full pipeline in the foreground, or use --only, --skip, and
--resume to control execution.
Wait for the command to reach a terminal outcome before returning.
Report the command, config, session, terminal outcome, and artifacts using
the output format.
# Run from Linux, a Linux container, or WSL2.source .venv/bin/activate
physics-agent run apps/physics_agent/configs/lightbulb.yaml --dry-run
physics-agent run apps/physics_agent/configs/lightbulb.yaml
Required Execution Lifecycle
Treat every required physics-agent invocation, including run, predict,
tune, refine, and dataset commands, as foreground work. Wait for the
process to exit before reporting completion or returning from a one-shot or
headless agent run.
Do not append &, use nohup, create an ordinary detached shell job, or
return while the required process is still tied to the current execution
session. Session teardown can kill that process before it produces results.
Do not promise to resume, poll, or wake up later through a mechanism that was
not actually created or cannot fire after the current agent run ends.
The only exception is a durable external monitor or handoff that the
execution surface actually provides and that survives session teardown.
Before returning, report its job or monitor ID, owner, log location, and
exact status command. A local PID or shell job is not a durable handoff.
If the foreground run cannot finish and no durable external monitor exists,
report that constraint and the exact foreground command instead of starting
work that will be abandoned.
Primary Command
physics-agent run <config.yaml> [OPTIONS]
Option
Description
--skip <steps>
Comma-separated steps to skip.
--only <steps>
Comma-separated steps to run exclusively.
--session-id <id>
Reuse or override the session ID.
--resume
Continue from the last successful checkpoint.
--dry-run
Show the pipeline plan without executing.
--clean
Delete the working directory before starting.
--verbose, -v
Enable debug logging.
--log-file <path>
Write logs to a file.
--log-level <level>
Override the default INFO log level.
Other Commands
Command
Description
physics-agent run <config.yaml>
Execute the unified multi-step pipeline. This is the primary command.
physics-agent predict <config.yaml>
Run the direct VLM prediction API on a prepared dataset.
physics-agent tune [SCENARIO.yaml]
Tune authored physics parameters against a simulator.
physics-agent refine <SCENARIO.yaml>
Iteratively run tune, judge the result, and refine the scenario.
physics-agent pipeline <config.yaml>
Deprecated alias for run; mention only for legacy reproduction.
Use predict when dataset rendering and preparation have already happened and
the user only needs VLM inference. It calls the prediction API directly instead
of routing through the unified pipeline.
physics-agent predict <config.yaml> [OPTIONS]
Option
Description
--dataset <path>, -d <path>
Override the prepared dataset.jsonl path from config.
--output <dir>, -o <dir>
Override the prediction output directory.
--resume
Resume from existing predictions.
--verbose, -v
Enable debug logging.
--log-file <path>
Write logs to a file.
--log-level <level>
Override the default INFO log level.
physics-agent run <config.yaml> --only predict remains supported when the
prediction step should execute inside the pipeline checkpoint system.
Tune Command
Use tune after apply_physics has authored a simulation-ready USD. It
patches candidate physics parameters, scores each candidate with the selected
simulation backend, and writes the best parameters plus a tuned USD.
physics-agent tune [SCENARIO.yaml] [OPTIONS]
Supply either SCENARIO.yaml, --user-prompt, or both. When both are present,
the explicit scenario YAML wins on field conflicts. --physics-usd is required
unless the scenario defines physics_usd:.
Option
Description
--user-prompt <text>
Author a scenario from natural language.
--physics-usd <path>
Physics-authored USD to tune.
--reference-image <path>
Add judge image evidence; can repeat.
--reference-video <path>
Add judge video evidence; can repeat.
--reference-description <text>
Description for a reference image; can repeat.
--reference-video-description <text>
Description for a reference video; can repeat.
--engine ovphysx|newton|fake
Simulation backend. Default is ovphysx.
--optimizer auto|botorch|random|cma-es
Optimizer. Default is auto.
--output-dir <dir>, -o <dir>
Destination for tune artifacts.
--max-trials <n>
Number of optimizer trials.
--seed <n>
Seed for optimizer and backend when supported.
--judge/--no-judge
Enable or disable the final VLM-as-judge pass.
--judge-max-iterations <n>
Metadata/pass-through only for single-shot tune; use refine for real iteration.
--judge-max-tokens <n>
Override judge response length.
--judge-temperature <value>
Override judge temperature.
--verbose, -v
Enable debug logging.
--log-file <path>
Write logs to a file.
--log-level <level>
Override the default INFO log level.
For production OvPhysX + BoTorch tuning, install the tuning extra and provide
an OvPhysX daemon environment. The default daemon venv is
~/.cache/wu/ovphysx_venv; override it with WU_OVPHYSX_VENV_DIR. For Newton,
install the newton extra and select --engine newton. For tests or a local
smoke check, use --engine fake --optimizer random.
physics-agent tune apps/physics_agent/configs/tuning/drop_settle.yaml \
--physics-usd output/physics/asset_physics.usda \
--engine ovphysx --optimizer auto --output-dir output/tune
physics-agent tune --user-prompt "make this object bouncy" \
--physics-usd output/physics/asset_physics.usda \
--engine ovphysx --optimizer random
Refine Command
Use refine when a single tune pass is not enough and the judge should be
allowed to rewrite the scenario for additional iterations. Each iteration runs
tune, asks the judge whether the result meets the threshold, and when needed
asks the configured chat backend to refine the next scenario.
physics-agent refine <SCENARIO.yaml> [OPTIONS]
The CLI requires both --physics-usd and --user-prompt.
Option
Description
--physics-usd <path>
Required physics-authored USD to tune.
--user-prompt <text>
Required natural-language target for refinement.
--reference-image <path>
Add judge image evidence; can repeat.
--reference-video <path>
Add judge video evidence; can repeat.
--reference-description <text>
Description for a reference image; can repeat.
--reference-video-description <text>
Description for a reference video; can repeat.
--no-visual-evidence
Judge without generated/reference media.
--output-dir <dir>, -o <dir>
Destination for per-iteration artifacts.
--engine ovphysx|newton|fake
Simulation backend passed through to tune.
--optimizer auto|botorch|random|cma-es
Optimizer passed through to tune.
--max-trials <n>
Tune trials per iteration.
--max-iterations <n>
Hard cap on tune/judge/refine iterations.
--score-threshold <value>
Combined score above which the judge approves.
--judge-max-tokens <n>
Override judge response length.
--judge-temperature <value>
Override judge temperature.
--seed <n>
Seed forwarded to each tune iteration.
--chat-backend <name>
Backend for scenario refinement and judge calls.
--chat-model <name>
Chat model identifier for the selected backend.
--llm-timeout-seconds <seconds>
Deadline for each judge/refine LLM call; 0 disables.
--verbose, -v
Enable debug logging.
--log-file <path>
Write logs to a file.
--log-level <level>
Override the default INFO log level.
The public default chat backend is gemini, which reads GOOGLE_API_KEY or
GEMINI_API_KEY. Internal installs can route through another registered backend
with --chat-backend and --chat-model.
build_dataset_prepare_dataset - assemble classification specs and images.
predict - predict per-component material, component type, and physical
properties.
restore_usd - remap predictions from optimized paths to original paths
for reporting and artifacts.
apply_physics - author RigidBodyAPI, CollisionAPI,
MeshCollisionAPI, MassAPI, and physics-purpose material bindings where
appropriate.
Common Workflows
# Run prediction only.
physics-agent run apps/physics_agent/configs/lightbulb.yaml --only predict
# Run direct prediction on an already prepared dataset.
physics-agent predict apps/physics_agent/configs/lightbulb.yaml \
--dataset apps/physics_agent/configs/.lightbulb/dataset/dataset.jsonl
# Skip optimization for a simple, already prepared USD.
physics-agent run apps/physics_agent/configs/lightbulb.yaml --skip optimize_usd
# Resume after a failed step.
physics-agent run apps/physics_agent/configs/lightbulb.yaml --resume
# Tune a physics-authored USD.
physics-agent tune apps/physics_agent/configs/tuning/drop_settle.yaml \
--physics-usd apps/physics_agent/configs/.lightbulb/physics/light_bulb_01_physics.usdz \
--engine ovphysx --optimizer auto
# Run iterative tuning and scenario refinement.
physics-agent refine apps/physics_agent/configs/tuning/drop_settle.yaml \
--physics-usd apps/physics_agent/configs/.lightbulb/physics/light_bulb_01_physics.usdz \
--user-prompt "make it settle without bouncing" \
--max-iterations 3
Instanced USD and Instance Proxies
Enable deinstance when an asset is instanced or apply_physics raises a
PhysicsAuthoringError mentioning an instance proxy:
If the instanced asset also needs separate component classification from one
combined mesh, enable both enable_deinstance: true and
enable_split_meshes: true.
Service Boundary
This skill runs the local CLI. Do not construct REST requests or manage service
containers from it. Use physics-agent-client for /pipeline, /predict,
/tune, and /refine requests; use deploy-physics-agent-docker or
deploy-physics-agent-brev for the matching deployment workflow. A local CLI
config may consume remote render or optimizer endpoints without becoming a
service-client or deployment workflow.
Config Authoring
Prefer copying apps/physics_agent/configs/lightbulb.yaml for new configs.
Adapt only the user-specific fields:
project.name and project.session_id
input.usd_path
steps.optimize_usd.scene_optimizer_settings
steps.predict.vlm.model
Keep renderer, prompt, and physics authoring settings unless the user
explicitly asks to change them.
Output Format
Report these items for every execution path:
Command executed and whether it was full pipeline, --only, --skip,
--resume, or --dry-run.
Config path and session ID, when created.
Working directory, usually .<session_id>/ next to the config unless
project.working_dir overrides it.
Key artifacts when present:
predictions/predictions.jsonl with id, classification, reasoning,
and optional quality_warnings.
restored_predictions.jsonl or restored prediction artifacts when
restore_usd ran.
physics/<stem>_physics.<derived extension> when apply_physics ran;
USDZ inputs default to USDA output unless a lower-level apply call used an
explicit .usdz output path.
Optimized/deinstanced USD artifacts when optimize_usd ran.
Tune artifacts: best_params.json, tune_results.json,
history.jsonl, report.md, and tuned_physics.usda.
Refine artifacts: iter_<N>/ directories, final/, and
refine_summary.json.
Any missing credentials, service endpoints, optimizer failures, empty
predictions, tuning/refinement failures, or instance-proxy authoring errors.
Foreground Terminal Run
Report the Physics Agent CLI terminal outcome (completed, failed, or
interrupted) and its exit code. A started process or partial log is not
evidence of completion.
Durable External Handoff
Report the handoff-creation command's terminal outcome and exit code. These
describe only whether handoff creation succeeded, not the external Physics
Agent job's eventual outcome.
Report the real external job ID and, when distinct, the monitor ID, plus the
owner, log location, exact status command, and currently observed external
job status. Label that status as non-terminal unless the external monitor
itself supplies terminal evidence.
Troubleshooting
Symptom
Cause
Fix
API key required
The selected VLM backend has no credential.
Set the required key locally or in .env; do not paste it into chat.
Pipeline fails midway
A step failed after writing partial artifacts.
Re-run with --resume; use --clean only when the user wants to discard prior artifacts.
PhysicsAuthoringError mentions an instance proxy
The prediction path targets a non-writable instance proxy.
Enable optimize_usd with deinstance and keep restore_usd enabled.
One combined mesh needs per-component physics
The asset needs splitting before rendering/prediction.
Enable enable_split_meshes along with deinstance when needed.
Relative paths resolve unexpectedly
Config paths resolve from the config file directory.
Rewrite paths relative to the config file or make them absolute.
Empty predictions are rejected
The pipeline produced zero records or all prediction calls failed.
Check dataset renders, VLM credentials, and predict.allow_empty_predictions before opting into empty outputs.
Mass values look suspicious
Scene scale may make inferred mass unreliable.
Review quality_warnings and the configured apply_physics.mass_scale_policy.
BoTorch or OvPhysX is unavailable
The tuning extra or isolated OvPhysX daemon environment is missing.
Install apps/physics_agent[tuning] and set up WU_OVPHYSX_VENV_DIR, or smoke-test with --engine fake --optimizer random.
Newton tuning fails at startup
The newton extra or a compatible GPU/runtime is missing.
Install apps/physics_agent[newton]; set PA_NEWTON_DEVICE=cpu only for CPU-capable checks.
refine fails before the first iteration
The selected chat backend has no usable key or model.
Set the backend key locally, or pass a registered --chat-backend and --chat-model.
