arXiv:2606.07999v1 Announce Type: new Abstract: Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible. This challenge is particularly pronounced in embodied settings, where agents must operate in dynamic, partially observable environments without access to large language models (LLMs). In this setting, reliance on LLMs is impractical, while small language models (sLMs) remain insufficient for the effective skill grounding required for reliable long-horizon control. W
The deployment of embodied agents in dynamic environments necessitates efficient skill grounding without constant reliance on large language models, pushing research into optimizing smaller models.
Achieving effective skill grounding with small language models (sLMs) is crucial for the practical, scalable, and autonomous operation of embodied agents in real-world scenarios.
This research suggests a pathway to making sLMs more capable for complex tasks like reliable long-horizon control, broadening the potential applications of embodied AI.
- · AI developers
- · Robotics companies
- · SME-focused AI solutions
- · Systems heavily reliant on large, centralized LLMs for embodied tasks
- · Developers unprepared for decentralized AI deployment
More widespread and cost-effective deployment of embodied AI in diverse and challenging environments.
Increased competition and innovation in the development of efficient and specialized small language models for robotics.
The acceleration of autonomous agents capable of performing complex physical tasks without continuous human oversight or massive computational infrastructure.
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Read at arXiv cs.AI