• Motion control. Unlike virtual environments, physical spaces impose realistic constraints on ac-
tions through embodiment. Action sequences generated by LLM-based agents should be adaptable
to the environment. It means that the physical environment necessitates executable and grounded
motion control [258]. For example, imagine an agent operating a robotic arm in a factory. Grasping
objects with different textures requires precision tuning and controlled force, which prevents
damage to items. Moreover, the agent must navigate the physical workspace and make real-time
adjustments, avoiding obstacles and optimizing the trajectory of the arm.

In summary, to effectively interact within tangible spaces, agents must undergo hardware-specific
and scenario-specific training to develop adaptive abilities that can transfer from virtual to physical
environments. We will discuss more in the following section (§ 6.5).

5.3 Society Simulation with LLM-based Agents
TheRiseandPotentialofLargeLanguageModel BasedAgents