Knowledge Retrieval for Robotic Cooking
This addresses the challenge of robotic cooking by improving task completion in a domain-specific context, though it appears incremental as it builds on existing FOON methods.
The paper tackles the problem of enabling robots to retrieve recipes and ingredients for cooking tasks by proposing weighted Functional Object-Oriented Networks and task planning algorithms, resulting in higher success rates for human-robot collaboration compared to humans alone.
Search algorithms are applied where data retrieval with specified specifications is required. The motivation behind developing search algorithms in Functional Object-Oriented Networks is that most of the time, a certain recipe needs to be retrieved or ingredients for a certain recipe needs to be determined. According to the introduction, there is a time when execution of an entire recipe is not available for a robot thus prompting the need to retrieve a certain recipe or ingredients. With a quality FOON, robots can decipher a task goal, find the correct objects at the required states on which to operate and output a sequence of proper manipulation motions. This paper shows several proposed weighted FOON and task planning algorithms that allow a robot and a human to successfully complete complicated tasks together with higher success rates than a human doing them alone.