  The last few years have witnessed substantial progress in the field of
embodied AI where artificial agents, mirroring biological counterparts, are now
able to learn from interaction to accomplish complex tasks. Despite this
success, biological organisms still hold one large advantage over these
simulated agents: adaptation. While both living and simulated agents make
decisions to achieve goals (strategy), biological organisms have evolved to
understand their environment (sensing) and respond to it (physiology). The net
gain of these factors depends on the environment, and organisms have adapted
accordingly. For example, in a low vision aquatic environment some fish have
evolved specific neurons which offer a predictable, but incredibly rapid,
strategy to escape from predators. Mammals have lost these reactive systems,
but they have a much larger fields of view and brain circuitry capable of
understanding many future possibilities. While traditional embodied agents
manipulate an environment to best achieve a goal, we argue for an introspective
agent, which considers its own abilities in the context of its environment. We
show that different environments yield vastly different optimal designs, and
increasing long-term planning is often far less beneficial than other
improvements, such as increased physical ability. We present these findings to
broaden the definition of improvement in embodied AI passed increasingly
complex models. Just as in nature, we hope to reframe strategy as one tool,
among many, to succeed in an environment. Code is available at:
https://github.com/sarahpratt/introspective.
