Being H05 Scaling Human Centric Robot Learning

---
name: being-h05-scaling-human-centric-robot-learning
title: "Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Transfer"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.12993"
keywords: [Learning]
description: "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..."
---

## 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.