The creation of a robot chef represents a grand challenge for the field of robotics. Cooking is one of the most important activities that takes place in the home, and a robotic chef capable of following arbitrary recipes would have many applications in both household and industrial environments. The kitchen environment is a semistructured proving ground for algorithms in robotics. It provides many computational challenges, such as accurately perceiving ingredients in cluttered environments, manipulating objects, and engaging in complex activities such as mixing and chopping. Yet it also allows for reasonable simplifying assumptions due to the inherent organization of a kitchen around a human-centric workspace, the consistency of kitchen tools and tasks, and the ordered nature of recipes. We envision a robotic chef, the BakeBot, which can collect recipes online, parse them into a sequence of low-level actions, and execute them for the benefit of its human partners. We present first steps towards this vision, by combining techniques for object perception, manipulation, and language understanding to develop a novel end-to-end robot system able to follow simple recipes and by experimentally assessing the performance of these approaches in the kitchen domain. 1 Problem StatementThis paper describes progress towards a robotic system which is able to read and execute simple recipes. The robot is initialized with a set of ingredients laid out on the table and a set of natural language instructions describing how to use those ingre-

In active matter systems, self-propelled particles can self-organize to undergo collective motion, leading to persistent dynamical behavior out of equilibrium. In cells, cytoskeletal filaments and motor proteins self-organize into complex...

In active matter systems, self-propelled particles can self-organize to undergo collective motion, leading to persistent dynamical behavior out of equilibrium. In cells, cytoskeletal filaments and motor proteins exhibit activity and self-organization into complex structures important for cell mechanics, motility, and division. Collective dynamics of cytoskeletal systems can be reconstituted using filament gliding experiments, in which cytoskeletal filaments are propelled by surface-bound motor proteins. These experiments have observed diverse behavior, including flocks, polar streams, swirling vortices, and single filament spirals. Recent experiments with microtubules and kinesin motor proteins found that the effective repulsive interaction between filaments can be tuned by crowding agents in solution, altering the collective behavior. Adding a crowder reduced filament crossing, promoted alignment, and led to a transition from active, isotropically oriented filaments to locally aligned polar streams. These results suggest that tunable soft repulsion can control active phase behavior, but how altering steric interactions and filament stiffness alter collective motion is not fully understood. Here we use simulations of driven filaments with tunable soft repulsion and rigidity in order to better understand how the interplay between filament flexibility and steric effects can lead to different active steady states. We identify swirling flocks, polar streams, buckling bands, and spirals, and describe the physics that govern transitions between these states. In addition to repulsion, tuning filament stiffness can promote collective behavior, and controls the transition between active isotropic filaments, locally aligned flocks, and polar streams.