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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).

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Preis unbestätigt★ 128 GitHub-StarsVerzeichnis aktualisiert · 6. Sept. 2026agent-skill

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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 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	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

Reference Files

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

Companion Skills

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

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)

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.

# 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

NEVER git push — local commits only
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()
replay_trial_robosuite.py uses --args. prefix (tyro wraps the args parameter)
MUJOCO_GL=egl + TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 for all runs
Log to docs/logs/YYYY-MM-DD.md after significant work
Update the experiment's skills/ (.claude/robosuite/training-law/skills/) after any new pattern discovered

Monitoring

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

Quelle ansehen ↗

Dateimetadaten

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).

Originaltext anzeigen

---
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
```

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Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 128 stars, 7 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: NVlabs/ASPIRE
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 1. Sept. 2026
Verzeichnis aktualisiert: 6. Sept. 2026
Anleitungspfad: aspire/sim/.claude/robosuite/training-law/SKILL.md @ f4c8939aab0a

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

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65/100

Vielversprechend

Vertrauen

63/100

Nur Sandbox

Audit

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Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 128 stars, 7 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Weitere Details

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      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 128 stars, 7 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 128 stars, 7 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 65,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use robosuite/training-law/SKILL in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "nvlabs-robosuite-training-law-skill (robosuite/training-law/SKILL)",
      "install_command": "npx skills add NVlabs/ASPIRE --skill robosuite/training-law/SKILL",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "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"
  }
}

Für Ersteller

Quelle des Eintrags

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: NVlabs
Quelle: NVlabs/ASPIRE
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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