My cleaners obviously stopped coming in lockdown and I have never got round to getting them back or to cleaning myself. The grisly results are obvious to anyone who has been to the house lately. I will try and get my marigolds on today or tomorrow, but I can't promise.
Posted by Thomas Lew, Research Intern, and Montserrat Gonzalez Arenas, Research Engineer, Google Research, Brain Team
Over the past several years, the capabilities of robotic systems have improved dramatically. As the technology continues to improve and robotic agents are more routinely deployed in real-world environments, their capacity to assist in day-to-day activities will take on increasing importance. Repetitive tasks like wiping surfaces, folding clothes, and cleaning a room seem well-suited for robots, but remain challenging for robotic systems designed for structured environments like factories. Performing these types of tasks in more complex environments, like offices or homes, requires dealing with greater levels of environmental variability captured by high-dimensional sensory inputs, from images plus depth and force sensors.
For example, consider the task of wiping a table to clean a spill or brush away crumbs. While this task may seem simple, in practice, it encompasses many interesting challenges that are omnipresent in robotics. Indeed, at a high-level, deciding how to best wipe a spill from an image observation requires solving a challenging planning problem with stochastic dynamics: How should the robot wipe to avoid dispersing the spill perceived by a camera? But at a low-level, successfully executing a wiping motion also requires the robot to position itself to reach the problem area while avoiding nearby obstacles, such as chairs, and then to coordinate its motions to wipe clean the surface while maintaining contact with the table. Solving this table wiping problem would help researchers address a broader range of robotics tasks, such as cleaning windows and opening doors, which require both high-level planning from visual observations and precise contact-rich control.
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