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robosuite/training-law/SKILL
Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
Registry indexed
Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
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ASPIRE — LLMs write Python code to control a robot via a structured API. Code runs in MuJoCo (Robosuite).
7 Robosuite tasks:
| Task | Type | Camera key |
|---|---|---|
cube_lifting | Single-arm | robot0_robotview |
cube_restack | Single-arm | robot0_robotview |
cube_stack | Single-arm | robot0_robotview |
nut_assembly | Single-arm | robot0_robotview |
spill_wipe | Single-arm | robot0_robotview |
two_arm_lift | Bimanual | robot0_robotview |
two_arm_handover | Bimanual | robot0_robotview |
| Mode | File | When |
|---|---|---|
| Robosuite Baseline | run-baseline.md | Collect baseline on all 7 tasks |
| Robosuite Fix Loop (train-law) | main-agent-prompt.md | Coordinator guide: dispatch one subagent per GPU to debug failures |
| Robosuite Fix Loop Subagent (train-law) | subagent-prompt.md | Self-contained prompt template for dispatching one task to a background subagent |
| File | Covers |
|---|---|
| run-baseline.md | Launch command, config paths, output structure for all 7 tasks |
| api-reference.md | Full API functions, output structure, TraceLogger format, source files |
| clean-task-slate.md | Checklist for resetting a task to clean slate before rerunning fix loop |
| Skill | Covers |
|---|---|
grasp | Pick-and-place code template, pre-grasp/lower/close/lift/place skeleton |
localize | Perception server (SAM3/Molmo) prompting strategy, per-object prompt registry |
transport | Motion patterns for moving objects between locations — multi-step waypoints, safe transit sequences, interpolated Cartesian moves, collision avoidance during transport |
Two venvs:
.venv-robosuite (Python 3.10) — Robosuite replay/eval: replay_trial_robosuite.py.venv-libero or .venv-perception — perception servers only (handled by start_perception_servers.sh)Always use .venv-robosuite/bin/python3 for Robosuite replay/eval. Never use system python after setup.
tmux new -s aspire-perception
cd "$ASPIRE_ROOT"
ASPIRE_PERCEPTION_PYTHON=.venv-libero/bin/python3 \
bash scripts/common/start_perception_servers.sh --with-molmo
for p in 8114 8115 8116 8122; do
echo "port $p: $(curl -s -o /dev/null -w '%{http_code}' --max-time 3 http://127.0.0.1:$p/health)"
done
# 404/non-000 = UP for 8114-8116, 000 = DOWN. Molmo health is /v1/models on 8122.
SAM3 uses gated Hugging Face weights; authenticate before startup. GraspNet
requires the pinned Contact-GraspNet submodule and the perception environment
to include --extra contactgraspnet; the startup script verifies and applies
the compatibility patch. Molmo starts by default; --with-molmo aborts unless
the perception environment provides a vllm executable, so pass --no-molmo
to skip it and give up point-prompt fallback. Keep
servers in tmux or another persistent terminal; one-off background shells can
exit and take child server processes down with them.
| Server | Port | GPU | Required for |
|---|---|---|---|
| SAM3 | 8114 | Configured GPU | All runs |
| GraspNet | 8115 | Configured GPU | All runs |
| PyRoKi | 8116 | CPU | All runs |
| Molmo | 8122 | Configured GPU | Point-prompt fallback |
GPU layout: Derive server and worker assignments from the active host configuration.
Scripts use tyro.cli with a named args parameter — require the --args. prefix.
The examples assume SIM_GPU, SIM_GPUS, and DEBUG_TRIAL_ID are set from
the active host configuration and development partition.
# Replay fix code on a single trial
MUJOCO_GL=egl CUDA_VISIBLE_DEVICES="$SIM_GPU" TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 \
.venv-robosuite/bin/python3 scripts/robosuite/replay_trial_robosuite.py \
--args.config env_configs/robosuite/cube_lifting_multimodel_aspire_traced.yaml \
--args.trial "$DEBUG_TRIAL_ID" \
--args.replay-code /tmp/fix_attempt.py \
--args.output-dir ./outputs/debug_fix
# Interactive REPL (all API functions in scope)
MUJOCO_GL=egl CUDA_VISIBLE_DEVICES="$SIM_GPU" TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 \
.venv-robosuite/bin/python3 scripts/robosuite/replay_trial_robosuite.py \
--args.config env_configs/robosuite/cube_lifting_multimodel_aspire_traced.yaml \
--args.trial "$DEBUG_TRIAL_ID" \
--args.interactive \
--args.output-dir /tmp/repl_out
Config path pattern: env_configs/robosuite/<task>_multimodel_aspire_traced.yaml — all 7 tasks (including two_arm_handover) follow this single scheme. See the task reference table in main-agent-prompt.md for exact paths.
Trial split: Read the development and held-out partitions from the active experiment config; keep them disjoint and lock held-out trials during debugging.
sim.data.body_xpos, sim.data.get_site_xpos, sim.data.set_joint_qpos, sim.model.*, sim.data.qpos, sim.forward(), env._step_once()replay_trial_robosuite.py uses --args. prefix (tyro wraps the args parameter)MUJOCO_GL=egl + TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 for all runsdocs/logs/YYYY-MM-DD.md after significant workskills/ (.claude/robosuite/training-law/skills/) after any new pattern discoverednvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader
for gpu in $SIM_GPUS; do
procs=$(nvidia-smi -i $gpu --query-compute-apps=pid --format=csv,noheader,nounits 2>/dev/null | grep -c '[0-9]')
if [ "$procs" -eq 0 ]; then echo "GPU $gpu: FREE"; else echo "GPU $gpu: BUSY ($procs processes)"; fi
done
name: robosuite/training-law/SKILL description: Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
---
name: robosuite/training-law/SKILL
description: Master reference for ASPIRE Robosuite experiments. Covers system overview, 7 tasks, setup, running experiments, debugging, full API reference, and all pipeline modes (Fix Loop, Baseline).
---
# ASPIRE Experiment Pipeline
---
## What Is ASPIRE
ASPIRE — LLMs write Python code to control a robot via a structured API. Code runs in MuJoCo (Robosuite).
**7 Robosuite tasks:**
| Task | Type | Camera key |
|---|---|---|
| `cube_lifting` | Single-arm | `robot0_robotview` |
| `cube_restack` | Single-arm | `robot0_robotview` |
| `cube_stack` | Single-arm | `robot0_robotview` |
| `nut_assembly` | Single-arm | `robot0_robotview` |
| `spill_wipe` | Single-arm | `robot0_robotview` |
| `two_arm_lift` | Bimanual | `robot0_robotview` |
| `two_arm_handover` | Bimanual | `robot0_robotview` |
---
## Pipeline Modes
| Mode | File | When |
|---|---|---|
| **Robosuite Baseline** | [run-baseline.md](../run-baseline.md) | Collect baseline on all 7 tasks |
| **Robosuite Fix Loop (train-law)** | [main-agent-prompt.md](main-agent-prompt.md) | Coordinator guide: dispatch one subagent per GPU to debug failures |
| **Robosuite Fix Loop Subagent (train-law)** | [subagent-prompt.md](subagent-prompt.md) | Self-contained prompt template for dispatching one task to a background subagent |
## Reference Files
| File | Covers |
|---|---|
| [run-baseline.md](../run-baseline.md) | Launch command, config paths, output structure for all 7 tasks |
| [api-reference.md](../api-reference.md) | Full API functions, output structure, TraceLogger format, source files |
| [clean-task-slate.md](clean-task-slate.md) | Checklist for resetting a task to clean slate before rerunning fix loop |
## Companion Skills
| Skill | Covers |
|---|---|
| [`grasp`](skills/grasp.md) | Pick-and-place code template, pre-grasp/lower/close/lift/place skeleton |
| [`localize`](skills/localize.md) | Perception server (SAM3/Molmo) prompting strategy, per-object prompt registry |
| [`transport`](skills/transport.md) | Motion patterns for moving objects between locations — multi-step waypoints, safe transit sequences, interpolated Cartesian moves, collision avoidance during transport |
---
## Setup
**Two venvs:**
- `.venv-robosuite` (Python 3.10) — Robosuite replay/eval: `replay_trial_robosuite.py`
- `.venv-libero` or `.venv-perception` — perception servers only (handled by `start_perception_servers.sh`)
**Always use `.venv-robosuite/bin/python3` for Robosuite replay/eval. Never use system python after setup.**
---
## Perception Servers (required before any experiment)
```bash
tmux new -s aspire-perception
cd "$ASPIRE_ROOT"
ASPIRE_PERCEPTION_PYTHON=.venv-libero/bin/python3 \
bash scripts/common/start_perception_servers.sh --with-molmo
for p in 8114 8115 8116 8122; do
echo "port $p: $(curl -s -o /dev/null -w '%{http_code}' --max-time 3 http://127.0.0.1:$p/health)"
done
# 404/non-000 = UP for 8114-8116, 000 = DOWN. Molmo health is /v1/models on 8122.
```
SAM3 uses gated Hugging Face weights; authenticate before startup. GraspNet
requires the pinned Contact-GraspNet submodule and the perception environment
to include `--extra contactgraspnet`; the startup script verifies and applies
the compatibility patch. Molmo starts by default; `--with-molmo` aborts unless
the perception environment provides a `vllm` executable, so pass `--no-molmo`
to skip it and give up point-prompt fallback. Keep
servers in tmux or another persistent terminal; one-off background shells can
exit and take child server processes down with them.
| Server | Port | GPU | Required for |
|---|---|---|---|
| SAM3 | 8114 | Configured GPU | All runs |
| GraspNet | 8115 | Configured GPU | All runs |
| PyRoKi | 8116 | CPU | All runs |
| Molmo | 8122 | Configured GPU | Point-prompt fallback |
**GPU layout:** Derive server and worker assignments from the active host configuration.
---
## Running Experiments
Scripts use `tyro.cli` with a named `args` parameter — require the **`--args.` prefix**.
The examples assume `SIM_GPU`, `SIM_GPUS`, and `DEBUG_TRIAL_ID` are set from
the active host configuration and development partition.
```bash
# Replay fix code on a single trial
MUJOCO_GL=egl CUDA_VISIBLE_DEVICES="$SIM_GPU" TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 \
.venv-robosuite/bin/python3 scripts/robosuite/replay_trial_robosuite.py \
--args.config env_configs/robosuite/cube_lifting_multimodel_aspire_traced.yaml \
--args.trial "$DEBUG_TRIAL_ID" \
--args.replay-code /tmp/fix_attempt.py \
--args.output-dir ./outputs/debug_fix
# Interactive REPL (all API functions in scope)
MUJOCO_GL=egl CUDA_VISIBLE_DEVICES="$SIM_GPU" TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 \
.venv-robosuite/bin/python3 scripts/robosuite/replay_trial_robosuite.py \
--args.config env_configs/robosuite/cube_lifting_multimodel_aspire_traced.yaml \
--args.trial "$DEBUG_TRIAL_ID" \
--args.interactive \
--args.output-dir /tmp/repl_out
```
**Config path pattern:** `env_configs/robosuite/<task>_multimodel_aspire_traced.yaml` — all 7 tasks (including `two_arm_handover`) follow this single scheme. See the task reference table in `main-agent-prompt.md` for exact paths.
**Trial split:** Read the development and held-out partitions from the active experiment config; keep them disjoint and lock held-out trials during debugging.
---
## Critical Rules
1. **NEVER git push** — local commits only
2. **No forbidden APIs**: `sim.data.body_xpos`, `sim.data.get_site_xpos`, `sim.data.set_joint_qpos`, `sim.model.*`, `sim.data.qpos`, `sim.forward()`, `env._step_once()`
3. **`replay_trial_robosuite.py` uses `--args.` prefix** (tyro wraps the `args` parameter)
4. **`MUJOCO_GL=egl`** + **`TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1`** for all runs
5. **Log** to `docs/logs/YYYY-MM-DD.md` after significant work
6. **Update the experiment's `skills/`** (`.claude/robosuite/training-law/skills/`) after any new pattern discovered
---
## Monitoring
```bash
nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader
for gpu in $SIM_GPUS; do
procs=$(nvidia-smi -i $gpu --query-compute-apps=pid --format=csv,noheader,nounits 2>/dev/null | grep -c '[0-9]')
if [ "$procs" -eq 0 ]; then echo "GPU $gpu: FREE"; else echo "GPU $gpu: BUSY ($procs processes)"; fi
done
```
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
68/100
Promising
Trust
64/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "nvlabs-robosuite-training-law-skill",
"task": "Use robosuite/training-law/SKILL in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/nvlabs-robosuite-training-law-skill",
"api": "https://www.openagentskill.com/api/agent/skills/nvlabs-robosuite-training-law-skill",
"audit": "https://www.openagentskill.com/skills/nvlabs-robosuite-training-law-skill/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=nvlabs-robosuite-training-law-skill&task=Use%20robosuite%2Ftraining-law%2FSKILL%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20robosuite%2Ftraining-law%2FSKILL%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20robosuite%2Ftraining-law%2FSKILL%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/nvlabs-robosuite-training-law-skill/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/nvlabs-robosuite-training-law-skill"
}
}Listing source
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Audit
78/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.