Original title: Physical Reasoning and Object Planning for Household Embodied Agents
Authors: Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu
In the article, researchers dive into the world of household robots, focusing on how these clever machines make decisions when picking alternative items for tasks. They create a cool new system called COAT that helps analyze how robots figure out what to use when things aren’t available. They look at how humans decide by making three sets of tricky questions for language-based models. By testing these models, they learn three big things: how well a chosen object fits the job, how social and safety rules affect the decision, and how an object’s condition matters. To make it easier, they make five “condition” types to mimic real-life scenarios. The study gives insight into how robots think when picking stuff and offers new datasets for others to use. This research doesn’t just teach us about how robots think—it also sets the stage for smarter household robots in the future.
Original article: https://arxiv.org/abs/2311.13577