Robot Cooking
| Title | Robot Cooking |
|---|---|
| Summary | Common sense for a robot to cook healthy food |
| Keywords | Robots, Healthcare, Visual Recognition |
| TimeFrame | 2017/1/1-2017/8/30 |
| References | -robot cooking
Christian Østergaard Laursen, Søren Pedersen, Timothy Merritt, Ole Caprani. Robot-Supported Food Experiences: Exploring Aesthetic Plating with Design Prototypes. In J.T.K.V. Koh et al. (Eds.): Cultural Robotics 2015, LNAI 9549, pp. 107–130, 2016. DOI: 10.1007/978-3-319-42945-8 10 Springer International Publishing Switzerland 2016.012.241 -common sense acquisition Rakesh Gupta, Mykel J. Kochenderfer. Common Sense Data Acquisition for Indoor Mobile Robots. ROBOTICS |
| Prerequisites | Strong multidisciplinary interest, strong work ethic, software (also ability to work with libraries), possibly some small work with hardware/electronics |
| Author | Chandrashekhar Shankarrao Nasurade, Vamsi Krishna Nathani |
| Supervisor | Martin Cooney |
| Level | Master |
| Status | Ongoing |
This project will be about designing a capability for "common sense" in a robot, within the context of helping an elderly person at home with cooking.
Our motivation is that robots could be helpful to people and contribute to their well-being, health, and quality of life, but first some major challenges must be overcome. One challenge is that everyday tasks which humans perform can be complex and confusing. This is especially a problem with elderly persons with declined physical and cognitive abilities (e.g. dementia), when the person can no longer function by themselves but requires support, which is sometimes not available from other humans. To support such a person, we believe a robot should have some degree of what we call here "common sense"; related to robustness, this is an ability to function correctly in the presence of some errors which a healthy adult person can typically detect.
In the case of cooking, to cook in a healthy and good way, this means that a robot should be able to detect and compensate for errors in three main facets of cooking: the recipe, the tools used, and the ingredients.
For example, a human can determine that if a recipe for one person calls for 10kg of salt, this is probably a mistake, and conclude from experience with similar recipes that it should be 10g of salt.
If a recipe calls for a spatula but instead a knife has been provided, a human can determine that this is wrong and seek from experience some tool which is more appropriate.
If instead of salt, a bag of sugar has been provided, a human can determine that this is wrong and seek more appropriate ingredients.
To address the challenge, in this project the robot will learn a model of common sense regarding these three main facets of cooking by unsupervised learning (forming clusters, detecting anomalies, and inferring how anomalies can be rectified).
The robot used will be Baxter on a Ridgeback mobile base, which will be shared with other students and researchers.
The evaluation will measure the degree to which the robot can complete a simple cooking task in the presence of various errors which we introduce into the cooking process.
Timeline:
January-February: Preparation: literature review; setting up basic capability for a robot to do a simple cooking task (formulate instructions from a recipe, detect tools and ingredients, and carry out instructions)
March-April: Main point:learning a common sense model to avoid errors
May: Evaluation, writing/presenting
Expected results: a thesis/report, code, video (we also hope to offer some food cooked by the robot)
