# Being H05 Scaling Human Centric Robot Learning

> We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a universal 'mother tongue' for physical interaction. To support this, we present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000...

- Skill: `adu2021/being-h05-scaling-human-centric-robot-learning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/being-h05-scaling-human-centric-robot-learning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/being-h05-scaling-human-centric-robot-learning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/being-h05-scaling-human-centric-robot-learning

---


## Overview

This skill covers research on being-h0.5: scaling human-centric robot learning for cross-embodiment transfer. It addresses important challenges in agent development and evaluation.

## Key Insights

The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners

## When to Use

Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement

## When NOT to Use

- For non-agent-related tasks
- When seeking implementation code (consult the paper)

## Resources

- ArXiv Abstract: https://arxiv.org/abs/2601.12993
- Full PDF: https://arxiv.org/pdf/2601.12993
- HTML: https://arxiv.org/html/2601.12993

Refer to the original paper for complete technical details, methodology, and experimental protocols.