  Everyday devices like light bulbs and kitchen appliances are now embedded
with so many features and automated behaviors that they have become complicated
to actually use. While such "smart" capabilities can better support users'
goals, the task of learning the "ins and outs" of different devices is
daunting. Voice assistants aim to solve this problem by providing a natural
language interface to devices, yet such assistants cannot understand
loosely-constrained commands, they lack the ability to reason about and explain
devices' behaviors to users, and they rely on connectivity to intrusive cloud
infrastructure. Toward addressing these issues, we propose thoughtful things:
devices that leverage lightweight, on-device language models to take actions
and explain their behaviors in response to unconstrained user commands. We
propose an end-to-end framework that leverages formal modeling, automated
training data synthesis, and generative language models to create devices that
are both capable and thoughtful in the presence of unconstrained user goals and
inquiries. Our framework requires no labeled data and can be deployed
on-device, with no cloud dependency. We implement two thoughtful things (a lamp
and a thermostat) and deploy them on real hardware, evaluating their practical
performance.
