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Pelican-VL

  • AI model
  • Humanoids and robot learning

Pelican-VL 1.0 is an open-source family of embodied multimodal brain models, from 7B to 72B parameters, that take visual and text input for robotics-oriented reasoning.

Made by Open X-Humanoid, China (checked on github.com/Open-X-Humanoid (opens in a new tab)).

Source on GitHub: github.com/Open-X-Humanoid/pelican-vl (opens in a new tab). Facts read at commit 717a1fc (opens in a new tab).

What it does

  • Processes images, videos and text to recognize objects, reason about physical and spatial relations, and predict function from scene context.
  • Trained on video and dynamic scene question answering to follow temporal order and object motion.
  • Aims at robot tasks such as grasping, navigation and human-robot interaction, producing action sequences with per-step feasibility.
  • Uses DPPO cyclic training, generating new hard samples after each reinforcement learning cycle to iteratively improve weaknesses.
  • Released weights for 7B and 72B on Hugging Face and ModelScope, with a 3B checkpoint announced in December 2025.
  • Includes a LoRA fine-tuning quick start built on the ms-swift framework, plus an evaluation reproduction guide.

Written by Robotheca from the project's README (opens in a new tab) at commit 717a1fc.

Getting started

Difficulty
Advanced

Maintenance

Status
Maintained: a commit in the last 12 months
Newest commit
2 Dec 2025 on rxc
Latest release
None published on GitHub

Checked on GitHub on 16 Oct 2026.

Robots on Robotheca that use it

No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.

Robotheca lists AI models as a reference: nothing is hosted or run here, and every fact links to where it was read.