CLiViS: Unleashing Cognitive Map through Linguistic-Visual Synergy for Embodied Visual Reasoning
arXiv:2506.17629v2 Announce Type: replace-cross Abstract: Embodied Visual Reasoning (EVR) seeks to follow complex, free-form instructions based on egocentric video, enabling semantic understanding and spatiotemporal reasoning in dynamic environments. Despite its promising potential, EVR encounters significant challenges stemming from the diversity of complex instructions and the intricate spatiotemporal dynamics in long-term egocentric videos. Prior solutions either employ Large Language Models (LLMs) over static video captions, which often omit critical visual details, or rely on end-to-end V
The AI research community is actively pushing the boundaries of embodied intelligence, and advancements in large language models and visual processing are converging to enable more sophisticated reasoning in dynamic environments.
This development addresses a critical challenge in creating truly autonomous AI agents capable of understanding and acting on complex instructions in the real world, moving beyond static video analysis.
Embodied Visual Reasoning will be able to follow more diverse and complex free-form instructions by integrating linguistic and visual information more effectively than prior methods.
- · AI research labs
- · Robotics companies
- · Developers of autonomous systems
- · Companies in logistics and automation
- · Companies relying on static, vision-only AI
- · Systems with limited instruction following capabilities
Embodied AI systems will gain significantly enhanced capabilities for interpreting and executing complex multi-modal instructions.
This improved reasoning will accelerate the development of more general-purpose and adaptable robots and AI assistants.
More sophisticated embodied AI could lead to automation in new domains, potentially impacting the nature of work requiring dynamic understanding and response.
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Read at arXiv cs.CL