Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
arXiv:2606.19297v1 Announce Type: new Abstract: Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low-level control. We introduce Act2Answer, a lightweight protocol that adapts VLM knowledge benchmarks to VLA evaluation by requiring agents to answer through action. Each question becomes a short tabletop episode where the
The rapid development of Vision-Language Models (VLMs) and their application to robotics necessitates a deeper understanding of knowledge transfer and retention when fine-tuned for embodied tasks.
Understanding how VLM's knowledge degrades or transfers to action-oriented models is critical for developing robust and intelligent AI agents capable of complex tasks in the real world.
This research introduces a specific protocol, Act2Answer, to rigorously measure commonsense and world knowledge in Vision-Language-Action (VLA) models, providing a more reliable evaluation method than previous approaches which conflated knowledge gaps with control issues.
- · AI researchers focusing on embodied intelligence
- · Robotics companies developing advanced agents
- · Developers of foundational Vision-Language Models
- · Companies relying on naive fine-tuning approaches for VLA models
- · Approaches that don't rigorously test knowledge retention in embodied AI
Improved VLA models that retain more commonsense and world knowledge during adaptation.
Faster development of general-purpose humanoid robots capable of understanding and executing complex instructions.
Acceleration of AI agents that can operate autonomously and intelligently in diverse physical environments, shifting numerous industry paradigms.
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Read at arXiv cs.LG