arXiv:2512.05693v2 Announce Type: replace-cross Abstract: Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse embodiments, action spaces, and observation configurations. Modeling such heterogeneity with a shared dense action module can induce negative transfer, particularly when action spaces or visual observations differ across data sources. We address this issue with HiMoE-VLA, a VLA framework built around a Hierarchical Mixture-of-Experts (HiMoE) action module. HiMoE uses Action-Space MoE layers at the i
This research builds on recent advancements in generalist AI models, specifically addressing challenges in integrating diverse robotic data for more robust control policies.
Advanced and generalist vision-language-action policies are critical for the deployment of versatile AI in real-world physical systems, including robotics.
The proposed HiMoE-VLA framework introduces a more efficient method for handling heterogeneous robotic demonstration data, potentially accelerating the development of truly general-purpose robots.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Companies relying solely on task-specific, narrow AI models
Improved performance and broader applicability of robotic systems due to better handling of diverse training data.
Faster development and reduced costs for building adaptable robots, leading to increased adoption in various industries.
Accelerated progress towards economically viable, general-purpose humanoid robots capable of diverse tasks in unstructured environments.
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Read at arXiv cs.AI