# Session: 2026-03-10 13:28:42 UTC

- **Session Key**: agent:main:main
- **Session ID**: f3c76837-bd0b-46c2-acbe-a1618cf732b9
- **Source**: telegram

## Conversation Summary

assistant: It's live and woven together. Refresh the page:

**http://100.109.173.109:8090/31-neural-lab/**

Sim tab — you'll see the **actual RL training agents** moving in the viewport. The status bar shows episode count, timesteps, and which team is being trained. The scores are real — hiders at +119, seekers at -119 mid-episode. Those are the actual PPO-controlled agents learning in real-time.

25M steps, all visible. Go sleep for real now 😄
user: [media attached: /home/pmello/.openclaw/media/inbound/file_197---fcd4b38f-cd96-4a49-8481-c2b87758f1f7.ogg (audio/ogg; codecs=opus) | /home/pmello/.openclaw/media/inbound/file_197---fcd4b38f-cd96-4a49-8481-c2b87758f1f7.ogg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) and ~ paths — they are blocked for security. Keep caption in the text body.
Conversation info (untrusted metadata):
```json
{
  "message_id": "5753",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Tue 2026-03-10 09:19 EDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

<media:audio>
assistant: Got it. Big pivot — you want to do this right with NVIDIA Isaac Sim + Isaac Lab instead of our homebrew pymunk setup. Makes total sense on a DGX Spark. Let me save everything and scope this out.
assistant: Both installed. Let me save everything and prepare the handoff:
assistant: All saved and committed. Here's where we stand:

**✅ Isaac Sim 5.1.0** — already installed at `/isaac-sim/`
**✅ Isaac Lab** — already at `/home/pmello/IsaacLab/`
**✅ DGX Spark GPU** — perfect for GPU-accelerated PhysX + rendering

This is the right call. Isaac Sim + Isaac Lab gives you:
- **PhysX GPU physics** — thousands of parallel environments
- **One viewport = one truth** — what you see IS what's training
- **Proper RL framework** (rl_games/RSL-rl) built for this exact use case
- **Way faster training** — GPU-parallelized vs our single-threaded CPU pymunk

All homebrew Neural Lab work is saved (27K episodes, checkpoints, the whole UI). Next session I'll verify Isaac Sim boots, set up the hide-and-seek environment in Isaac Lab, and launch a proper training run where the viewport shows the real thing.

Also heads up — your Anthropic API credits might be running low. The morning briefing cron couldn't run.
user: [media attached: /home/pmello/.openclaw/media/inbound/file_198---de41e4d4-c070-4314-87a1-1c98889b354f.ogg (audio/ogg; codecs=opus) | /home/pmello/.openclaw/media/inbound/file_198---de41e4d4-c070-4314-87a1-1c98889b354f.ogg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) and ~ paths — they are blocked for security. Keep caption in the text body.
Conversation info (untrusted metadata):
```json
{
  "message_id": "5757",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Tue 2026-03-10 09:22 EDT"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

<media:audio>
assistant: Two big tasks:
1. **Isaac Sim setup** — hide-and-seek environment with proper RL
2. **Knowledge transfer to Jess** — full understanding of everything we've built, adapted to her workspace

Let me start both. First, let me find Jess's workspace and the shared tools space:
assistant: Now let me check what the shared tools/skills space looks like — this is where Pat wants tools accessible to all agents:
assistant: Good — I have the picture. This is a substantial task. Let me handle both pieces efficiently. I'll write the comprehensive knowledge transfer for Jess first (since that's memory files + docs), then tackle Isaac Sim setup:
assistant: Now update Jess's MEMORY.md with the comprehensive knowledge:
assistant: Now update Jess's TOOLS.md with the new tools:
assistant: Now update the shared context so all agents know about the Isaac Sim pivot:
assistant: Now let me tackle Isaac Sim. Let me verify it can actually launch:
assistant: This is a substantial setup. Isaac Lab already has multi-agent support and an SB3 training script. The path is clear but this needs real engineering — creating a custom hide-and-seek environment in Isaac Lab's framework. Let me save everything comprehensively and prepare for context clearing:
assistant: OK Pat, here's where things stand:

**✅ Done:**
- All work saved and committed
- Jess has been updated with full knowledge: MEMORY.md (service ports, Neural Lab, Isaac Sim), TOOLS.md (all APIs + commands), and a detailed knowledge transfer doc in `memory/2026-03-10-neural-lab-knowledge.md`
- SHARED_CONTEXT.md updated so all agents know about the Isaac Sim pivot
- Isaac Sim recon complete

**🔍 What I found:**
- **Isaac Sim 5.1.0** ✅ installed, Python 3.11 ready
- **Isaac Lab** ✅ installed, has multi-agent support + SB3/rl_games/skrl training scripts
- Need to create a custom `hide_and_seek` environment as an Isaac Lab "direct" env
- Isaac Lab has `DirectMARLEnv` for multi-agent — exactly what we need
- The SB3 train script is ready to go: `isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py --task <task>`

**Next steps (after context clear):**
1. Run `post_install.sh` and verify Isaac Sim boots
2. Create the hide-and-seek env in Isaac Lab's direct env framework
3. Wire up PPO training + live viewport
4. Launch the real 25M+ run

Ready for context clear whenever you want. RESUME.md has the full plan.