robosuite-training-law-skill

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

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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

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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.

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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.

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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

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

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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