arXiv:2601.20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift. We investigate whether general-purpose large language model (LLM) agent frameworks, originally developed for software engineering, can serve as an alternative control paradigm for embodied manipulation. We introduce FAEA (Frontier Agent as Embodied Agent), which applies an LLM agent framework directly to embo
The rapid advancements in large language models and concurrent progress in robotic manipulation are converging, making novel control paradigms possible. Current reliance on demonstration-based learning for robots is proving inefficient for widespread adoption.
This development suggests a pathway to more generalized and autonomous robotic control, potentially reducing the need for extensive, task-specific human intervention. It could accelerate the deployment of robots in unpredictable environments and tasks.
Robotics may shift from highly specialized, human-demonstration-dependent learning to more adaptive control frameworks leveraging LLM agency. This could broaden the applicability and reduce the development costs of robotic systems.
- · Robotics companies
- · Logistics and manufacturing sectors
- · AI software developers
- · Companies with complex unstructured physical tasks
- · Traditional robotics integrators focused on fixed tasks
- · Companies reliant on highly repetitive, single-purpose industrial robots
- · Manual labor in unpredictable environments
LLM agents begin controlling a wider range of robotic tasks without prior human demonstrations.
Reduced cost and increased flexibility in deploying robotic systems lead to greater automation across industries.
The definition of 'robot' expands beyond programmed machines to include adaptive, agentic systems capable of complex physical reasoning.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG