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-
This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition and manipulation as well as challenges that This work was done at the
This paper describes the architecture and implementation of a distributed autonomous gardening system. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. Task allocation, sensing and manipulation are distributed in the system and de-centrally coordinated. We describe the architecture of this system and present experimental results for navigation, object recognition and manipulation.
The Leveraged Freedom Chair (LFC) is a low-cost, all-terrain, variable mechanical advantage, lever-propelled wheelchair designed for use in developing countries. The user effectively changes gear by shifting his hands along the levers; grasping near the ends increases torque delivered to the drivetrain, while grasping near the pivots enables a larger angular displacement with every stroke, which increases angular velocity in the drivetrain and makes the chair go faster. This paper chronicles the design evolution of the LFC through three user trials in East Africa, Guatemala, and India. Feedback from test subjects was used to refine the chair between trials, resulting in a device 9.1 kg (20 lbs) lighter, 8.9 cm (3.5 in) narrower, and with a center of gravity 12.7 cm (5 in) lower than the first iter- * Address all correspondence to this author. ation. Survey data substantiated increases in performance after successive iterations. Quantitative biomechanical performance data were also measured during the Guatemala and India trials, which showed the LFC to be 76 percent faster and 41 percent more efficient during a common daily commute and able to produce 51 percent higher peak propulsion force compared to conventional, pushrim-propelled wheelchairs.
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