OpenVLA OFT Fine Tuning and Evaluation
Skill Verifiziert AktivFine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.
Enables researchers and engineers to reproduce OpenVLA-OFT paper results, train custom VLA action heads, and deploy server-client inference for robotics applications.
Funktionen
- Fine-tuning and evaluation of OpenVLA-OFT and OFT+
- LoRA adaptation for efficient fine-tuning
- Continuous action heads (L1 regression or diffusion)
- FiLM conditioning for enhanced language grounding (OFT+)
- Support for LIBERO simulation and ALOHA real-world setups
- Server-client deployment for ALOHA inference
- Detailed troubleshooting for common issues and invariants
Anwendungsfälle
- Reproducing OpenVLA-OFT paper results
- Training custom VLA action heads (L1 or diffusion)
- Deploying server-client inference for ALOHA robots
- Debugging normalization, LoRA merge, and cross-GPU issues
Nicht-Ziele
- General LLM fine-tuning without robot action heads
- Fine-tuning other VLA architectures (e.g., pi0/pi0.5 models)
- Using the NVIDIA Cosmos Policy stack
Praktiken
- Model Fine-Tuning
- Robot Action Generation
- Simulation Environments
- Real-World Deployment
- Model Evaluation
- Troubleshooting
Voraussetzungen
- Python 3.10+
- PyTorch 2.2.0+
- Transformers >=4.40.0
- PEFT ==0.11.1
- Specific GPU VRAM requirements (see SKILL.md)
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skillsQualitätspunktzahl
VerifiziertVertrauenssignale
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