Taste-Enabled Robotic Chef - On Robots Learning to Cook from Taste Feedback and Human Demonstration

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 project 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 project 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 project 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 project presents significant progress in research into robotic chefs, contributing 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.

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.