With advances in the field of robotic manipulation, sensing and machine learning, robotic chefs are expected to become prevalent in our kitchens and restaurants. Robotic chefs are envisioned to replicate human skills in order to reduce the burden of the cooking process. However, the potential of robots as a means to enhance the dining experience is unrecognised. This work introduces the concept of food quality optimization and its challenges with an automated omelette cooking robotic system. The design and control of the robotic system that uses general kitchen tools is presented first. Next, we investigate new optimization strategies for improving subjective food quality rating, a problem challenging because of the qualitative nature of the objective and strongly constrained number of function evaluations possible. Our results show that through appropriate design of the optimization routine using Batch Bayesian Optimization, improvements in the subjective evaluation of food quality can be achieved reliably, with very few trials and with the ability for bulk optimization. This study paves the way towards a broader vision of personalized food for taste-and-nutrition and transferable recipes.
Robotic fruit harvesting requires dexterity to handle delicate crops and development relying upon field testing possible only during the harvesting season. Here we focus on raspberry crops, and explore how the research methodology of harvesting robots can be accelerated through soft robotic technologies. We propose and demonstrate a physical twin of the harvesting environment: a sensorized physical simulator of a raspberry plant with tunable properties, used to train a robotic harvester in the laboratory regardless of season. The sensors on the twin allow for direct comparison with human demonstrations, used to tune the robot controllers. In early field demonstrations, an 80% harvesting success rate was achieved without any modifications on the lab trained robot.
The use of robotic systems for harvesting of crops is a growing application domain in the agriculture sector. A key challenge is to develop robotic systems to harvest soft fruits such as raspberries which require delicate handling as they are easily damaged. Designing and optimizing a robotic harvesting setup by testing on real raspberry crops can be challenging due to the short natural harvesting period and the cost and logistical challenges of running experiments in the field. To solve this problem, we present a sensorized physical twin of a raspberry which can be used to develop robotic harvesting systems before deploying in the field. The sensorized raspberry has the capability of measuring the applied forces before and after it has been picked off the plant with a high sensitivity. The mechanical design was optimized and a material with properties similar to the real fruit was chosen, in order to achieve similar mechanical properties to a real raspberry, specifically the stiffness before and after picking and the pulling force. The paper concludes with a harvesting demonstration performed by a robotic gripper, where the sensorized raspberry is used to assess the quality of the picking action. This work aims to lay the groundwork for accelerating the future development of robotic harvesting systems to enable robust development in a lab before deployment in the field.
Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance.
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