Hermes Embodied: Self-Improving Robotics via Hermes Agent

"Any robot owner can fine-tune a state-of-the-art VLA by talking to their agent. No ML expertise needed."

What Is This?

Hermes Embodied turns Hermes Agent into a self-improving robotics trainer. It adds three Hermes skills that close the loop between robot execution, training data collection, and model improvement — all orchestrated through natural language.

The same self-improvement loop that Hermes uses to get better at coding tasks (via Tinker-Atropos RL) now extends to physical robot control via Vision-Language-Action models.

Architecture

┌─────────────────────────────────────────────────────┐
│                   HERMES AGENT                       │
│  (Reasoning Layer — plans, monitors, orchestrates)   │
├─────────────────────────────────────────────────────┤
│                                                      │
│  ┌──────────┐  ┌──────────────┐  ┌───────────────┐  │
│  │ vast-gpu  │  │  vla-trainer │  │  robot-loop   │  │
│  │  (skill)  │  │   (skill)    │  │   (skill)     │  │
│  │           │  │              │  │               │  │
│  │ Provision │  │ SmolVLA /    │  │ Deploy model  │  │
│  │ & manage  │  │ GR00T fine-  │  │ Collect traj  │  │
│  │ cloud GPU │  │ tuning on    │  │ Auto-retrain  │  │
│  │ instances │  │ LeRobot data │  │ when improved │  │
│  └──────────┘  └──────────────┘  └───────────────┘  │
│                                                      │
├─────────────────────────────────────────────────────┤
│              SIMULATION / HARDWARE                   │
│                                                      │
│  MuJoCo + LeRobot gym_hil    OR    SO-ARM101 + USB  │
│  (Franka Panda sim tasks)          (Physical arm)    │
└─────────────────────────────────────────────────────┘

The Self-Improvement Loop

1. Deploy — Hermes loads a VLA checkpoint and runs it in sim (or on hardware)
2. Collect — Every rollout is recorded as a LeRobot trajectory (state, action, camera, reward)
3. Curate — Hermes filters successful trajectories (reward > threshold)
4. Train — Provisions a GPU on Vast.ai and fine-tunes SmolVLA on the new data
5. Evaluate — Runs open-loop eval comparing new checkpoint vs. old
6. Promote — If new model is better, it becomes the active policy
7. Repeat — Scheduled via Hermes cron, runs autonomously

Skills

vast-gpu — Cloud GPU Infrastructure

Provision, monitor, and teardown GPU instances on Vast.ai through natural language.

• "Spin up an A100 for training" → finds cheapest A100, creates instance, returns SSH access
• "How's my training instance?" → checks status, GPU utilization, cost so far
• "Tear down the GPU" → destroys instance, confirms billing stopped

vla-trainer — VLA Fine-Tuning Pipeline

End-to-end fine-tuning of Vision-Language-Action models.

• Supports SmolVLA (450M, fast) and GR00T N1.5 (3B, powerful)
• Handles data prep, LeRobot format conversion, stats validation
• Runs training on Vast.ai with WandB monitoring
• Open-loop evaluation with trajectory visualization

robot-loop — Continuous Improvement

The autonomous improvement cycle.

• Runs VLA inference in MuJoCo simulation
• Collects and scores trajectories
• Triggers retraining when enough new data accumulates
• A/B tests new checkpoints against current best
• Promotes winners, logs everything

Quick Start

# Tell Hermes what you want
"Set up a simulation environment for pick-and-place tasks"

# Hermes installs MuJoCo, LeRobot, configures the Franka Panda env

"Train SmolVLA on the pick-and-place demo dataset"

# Hermes provisions a Vast.ai GPU, downloads data, runs fine-tuning

"Deploy the trained model and start the improvement loop"

# Hermes runs inference in sim, collects trajectories, schedules retraining

Hardware Support (Optional)

For physical deployment on SO-ARM101:

• Leader arm (teleoperation/demo recording)
• Follower arm (autonomous execution)
• USB cameras (wrist + global view)
• Any Linux machine with USB ports

Models Supported

Model	Params	Train Time (A100)	VRAM	Best For
SmolVLA	450M	~4h / 20k steps	22GB	Fast iteration, prototyping
GR00T N1.5	3B	~4h / 10k steps	25GB	Production, complex tasks
GR00T N1.6	3B	~4h / 10k steps	25GB	Latest, best performance

Cost Estimate

• Vast.ai A100 80GB: ~$1/hr → ~$4 per training run
• Vast.ai A6000 48GB: ~$0.50/hr → ~$2 per training run
• Simulation: Free (local CPU/GPU)
• Physical arm (optional): ~$200-$440

Project Structure

hermes-embodied/
├── README.md
├── skills/
│   ├── vast-gpu/
│   │   └── SKILL.md
│   ├── vla-trainer/
│   │   └── SKILL.md
│   └── robot-loop/
│       └── SKILL.md
├── scripts/
│   ├── setup_sim.py          # MuJoCo + LeRobot environment setup
│   ├── collect_trajectories.py # Run VLA in sim, save rollouts
│   ├── train_smolvla.py      # Fine-tuning wrapper
│   ├── evaluate.py           # Open-loop eval + metrics
│   └── improvement_loop.py   # Full autonomous loop
├── configs/
│   ├── sim_env.json          # Simulation environment config
│   ├── training.yaml         # Training hyperparameters
│   └── vast_instance.yaml    # GPU instance specs
└── docs/
    └── ARCHITECTURE.md

Built With

• Hermes Agent — AI agent framework with skills, memory, and RL training
• LeRobot — Open-source robotics framework by Hugging Face
• SmolVLA — 450M parameter Vision-Language-Action model
• Vast.ai — Affordable cloud GPU rental
• MuJoCo — Physics simulation for robotics
• WandB — Experiment tracking

License

MIT