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Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Do Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding. This paper presents a mechanistic interpretability study on the language grounding capabilities of two state-of-the-art Vision-Language-Action models, π₀.₅ and GR00T N1.7. The study applies activation and attribution pat… Robotics — Robot learning news discovery: A Curated List of Vision-Language-Action (VLA) Models for Robotics and Embodied AI. This GitHub repository is a curated list of Vision-Language-Action (VLA) models, architectures, benchmarks, and datasets for robotics and embodied AI, with a focus on 2025–2026. It includes foundational models like RT-1,…
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported duration: 20000 hours · Basis not reported
“l upper-body humanoid control (fingers/wrists/torso/head) via a slow System-2 VLM plus an 80M fast System-1 controller, even coordinating two robots (2025). LingBot-VLA: A Pragmatic VLA Foundation Model — A cost-efficient foundation model trained on ~20,000 hours of dual-arm data across 9 robot configurations, with open code/models and strong cross-platform generalization (2026). X-VLA: Soft-Prompted Transformer as S”
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Evidence dependency changes (1)
- 2026-10-09T07:40:40.363Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
Developments
- 2026-10-08 04:00 UTC · 1 reportsDo Vision-Language-Action Models Understand Instructions? AarXiv Robotics — research abstracts：Do Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding
- 2026-10-05 17:58 UTC · 1 reportsEvent updateRobotics — Robot learning news discovery：A Curated List of Vision-Language-Action (VLA) Models for Robotics and Embodied AI
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- arXiv Robotics — research abstractsDo Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding
This paper presents a mechanistic interpretability study on the language grounding capabilities of two state-of-the-art Vision-Language-Action models, π₀.₅ and GR00T N1.7. The study applies activation and attribution patching to analyze how these models respond to variations in task instructions, including synonym replacement, semantic scaling, directional corruption, random object substitution, and empty strings. Results show differing sensitivities to abstract rephrasing and directional language, with distinct patterns of activation in each model.
- Robotics — Robot learning news discoverySignalA Curated List of Vision-Language-Action (VLA) Models for Robotics and Embodied AI
This GitHub repository is a curated list of Vision-Language-Action (VLA) models, architectures, benchmarks, and datasets for robotics and embodied AI, with a focus on 2025–2026. It includes foundational models like RT-1, RT-2, and OpenVLA, as well as advanced models such as π0, GR00T N1, and Gemini Robotics. The repository also features architectural innovations, benchmarks like LIBERO and RoboTwin 2.0, and open-source code for many of the models.
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