ADu2021/being-h05-scaling-human-centric-robot-learning
skills/skillxiv-v0.0.2-claude-opus-4.6/being-h05-scaling-human-centric-robot-learning/SKILL.md
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...
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SKILL.md
- 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.
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