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Taste-Enabled Robotic Chef On Robots Learning to Cook from Taste Feedback and Human Demonstration https://doi.org/10.17863/CAM.113797 Sochacki, Grzegorz Iida, Fumiya Cooking and consuming food is an important part of human society and culture. Regardless of technological advances, food preparation is still a time-consuming chore most people do daily. Cooking could be automated by introducing robotic chefs, which are robots capable of cooking a significant selection of dishes. This thesis focuses on exploring how hardware, both actuating and sensing, works in conjunction with control and machine learning algorithms to form a feedback loop in the context of cooking. Robotic chef faces many challenges including sensing properties of food, manipulation and learning from a limited amount of data, but the biggest challenge is the subjective nature of assessing the outcome of cooking. This problem is inescapable as the final dish is judged by the diner who is inherently subjective and the same dish may have a very different palatability for different diners. This thesis contributes to research in sensing and learning of the state and palatability of a dish cooked by a robot. It includes using tactile sensing in a robot that presented a raw and well-cooked vegetable to assess readiness and predict the course of further cooking. The thesis also discusses the use of electronic taste as feedback in the cooking process, where the robot replicates a variation of a dish preferred by a human diner. It was also proven that replication of the chewing process improves electronic taste and allows better classification between variations of dishes. The use of cameras to program robotic chefs by visual demonstration is also elaborated. Novel methods of machine learning for food palatability assessment are also discussed. Finally, most of the methods and systems presented have some subjective input from a human that allows the robot to deal with the subjectivity of food taste by catering to this specific person. In summary, the thesis presents significant progress in research into robotic chefs, con- tributing to all parts of robotic chefs including manipulation, sensing, signal processing and learning. Moreover, it is the first work that tackles robotic cooking with the use of electronic taste and catering to the specific and subjective preferences of a human diner. University of Cambridge 2024-01-22 Thesis Doctoral Doctor of Philosophy (PhD) eng Agriforwards CDT BEKO Plc https://www.repository.cam.ac.uk/handle/1810/376635 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/3a7a4e17-9525-4593-af9b-a4f525437420/download 6c3930c6ca2ba3268817bfa3d09ad225 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/77c6e924-7f76-4ac9-bc03-fb9464efc42b/download 87eda9de84448d1f82354d60eee3eb5f https://www.rioxx.net/licenses/all-rights-reserved/ Robotic Chef Test Sensor Robotic Cooking Taste Feedback Learning from Demonstration