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.