VisualActBench: Evaluating Visual Language Models' Action Capabilities

This ArXiv paper introduces VisualActBench, a benchmark designed to assess the action-taking abilities of Vision-Language Models (VLMs). The research focuses on the crucial aspect of embodied AI, exploring how VLMs can understand visual information and translate it into practical actions.

• •VisualActBench evaluates the ability of VLMs to perform actions based on visual input.
• •The research explores the performance of VLMs in embodied AI tasks.
• •The benchmark offers a way to measure progress in developing VLMs that can 'see and act'.