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fine-tuning-openvla-oft

Fine-tune and evaluate OpenVLA-OFT/OFT+ for robot action generation with visual-language inputs.

Updated Aug 23, 2026
Search Tags:#deployment#fine-tuning#robotics#lora#libero#openvla-oft#aloha
One-click install
npx skills add https://github.com/box755/simlens-research --skill fine-tuning-openvla-oft-box755
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-openvla-oft
Source: https://github.com/box755/simlens-research/tree/main/skill-packs/AI-Research-SKILLs/18-multimodal/openvla-oft
Command: npx skills add https://github.com/box755/simlens-research --skill fine-tuning-openvla-oft-box755

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tuning and evaluating OpenVLA-OFT/OFT+ for continuous robot action generation is hard to reproduce, especially across LIBERO simulation and ALOHA real-world deployments.

Core Features & Use Cases

  • OpenVLA-OFT/OFT+ training workflows: LoRA-based adaptation of OpenVLA with continuous action heads (L1 regression or diffusion) for robot control.
  • LIBERO evaluation pipelines: Run task-suite evaluations with paper-aligned settings (e.g., image cropping, action chunking, and normalization).
  • ALOHA server-client deployment guidance: Deploy an OFT+ policy on a GPU server and query actions from a robot-side client for real-robot testing.

Quick Start

Run a paper-aligned LIBERO evaluation for a pretrained OpenVLA-OFT checkpoint on a selected task suite.

Frequently Asked Questions about fine-tuning-openvla-oft

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune OpenVLA-OFT with LoRA for continuous robot action generation?▼

To fine-tune OpenVLA-OFT with LoRA, you apply LoRA-based adaptation to the OpenVLA model using continuous action heads, such as L1 regression or diffusion, configured with FiLM conditioning for visual-language robot control inputs.

What is the best way to evaluate OpenVLA-OFT policies on the LIBERO benchmark?▼

Evaluating OpenVLA-OFT on the LIBERO benchmark involves running task-suite evaluations with paper-aligned settings, ensuring strict consistency in image cropping, action chunking, and normalization key configurations.

Can I deploy an OpenVLA-OFT policy on a GPU server for ALOHA real-world robot testing?▼

Yes, you can deploy an OpenVLA-OFT+ policy on a GPU server and query actions from a robot-side client using the ALOHA server-client inference architecture for real-world robotic policy evaluation.

Why do my OpenVLA-OFT training and inference action predictions mismatch?▼

Training-to-inference mismatches in OpenVLA-OFT occur due to inconsistent preprocessing, image streams, action chunking, or un-normalization keys, requiring strict consistency checks during policy deployment and evaluation.

Do I need to merge LoRA adapters before deploying OpenVLA-OFT for robot control?▼

Merging LoRA adapters is part of the supported OpenVLA-OFT workflow, allowing you to consolidate fine-tuned weights before deploying the continuous action generation policy for inference.

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