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Researchers have created benchmarks for 100 everyday household tasks for robot assistants, creating a path for more useful agents.
Robots that do everything from helping people get dressed in the morning to washing (and putting away) the dishes have been a dream for as long people have uttered the words “artificial intelligence.”
But, in a field where the state of the art currently rests far short of that level of sophistication, a fundamental challenge has emerged: Namely, what will “success” even look like, should the day come when robots are able to perform these key tasks to human standards.
To do these mundane but surprisingly complex tasks, a robot must be able to perceive, reason, and operate with full awareness of its own physical dimension and capabilities, but also of the world and objects around it. In robotics, this combination of situational and physical awareness and capability is known as embodied AI.
Now, researchers have released the Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (BEHAVIOR).
It is a catalog of the physical and intellectual details of 100 everyday household tasks—washing dishes, picking up toys, cleaning floors, etc.—and an implementation of those tasks in multiple simulated homes.
A paper describing BEHAVIOR was recently accepted to the Conference on Robot Learning (CoRL).
Robot assistants at home
BEHAVIOR imbues a set of realistic, varied, and complex activities with a new logical and symbolic language, a fully functional 3D simulator with a virtual reality interface, and a set of success metrics drawn from the performance of humans doing the same tasks in virtual reality. Taken as a whole, BEHAVIOR delivers a breadth of tasks and a level of detailed descriptions about each task that was previously unavailable in AI.
“While any one of those tasks is already highly complex in its own right, imagine the challenge of creating a single robot that can do all of these things,” says Jiajun Wu, assistant professor of computer science and a senior author of the paper. “Creating these benchmarks now, before the field has evolved too far, will help to set up potential common goals for the community.”
Imagine the multiple problems a robot has to overcome to achieve a simple task like cleaning a countertop.
The robot not only has to perceive and understand what a countertop is, where to find it, that it needs cleaning, and the counter’s physical dimensions, but also what tools and products are best used to clean it and how to coordinate its motions to get it clean. The robot would then have to determine the best course of action, step by step, needed to clean the counter. It even requires a complex understanding of things humans think nothing of, such as what tools or materials are “soakable” and how to detect and declare a countertop “clean.”
In BEHAVIOR, this level of complexity is achieved in 100 activities performed in multiple different simulated houses.
Each of these steps (navigation, search, grasping, cleaning, evaluating) may require hours or even days of training in simulation to be learned—far beyond the capabilities of current autonomous robots.
“Deciding the best way to achieve a goal based on what the robot perceives and knows about the environment and about its own capabilities is …….
Source: https://www.futurity.org/robot-assistants-household-tasks-2663422/