Conjecture—Physical constraints yield a different learning landscape for embodied agents

Clarify and formalize how physical constraints in embodied agents produce a distinct learning landscape and technical requirements compared to non-embodied AI systems, and determine implications for learning methodology.

Background

Embodied agents exchange energy with the physical world, face safety-critical consequences of actions, and operate under resource constraints, which may fundamentally change learning requirements.

The authors note a longstanding conjecture that these physical constraints lead to a different learning landscape than traditional AI, motivating tailored methodological advances.