Improving Robotic Cooking using Batch Bayesian Optimization

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

Control and Morphology Optimization of Passive Asymmetric Structures for Robotic Swimming

Aquatic creatures exhibit remarkable adaptations of their body to efficiently interact with the surrounding fluid. The tight coupling between their morphology, motion, and the environment are highly complex but serves as a valuable example when creating biomimetic structures in soft robotic swimmers. We focus on the use of asymmetry in structures to aid thrust generation and maneuverability. Designs of structures with asymmetric profiles are explored so that we can use morphology to `shape' the thrust generation. We propose combining simple simulation with automatic data-driven methods to explore their interactions with the fluid. The asymmetric structure with its co-optimized morphology and controller is able to produce 2.5 times the useful thrust compared to a baseline symmetric structure. Furthermore these asymmetric feather-like arms are validated on a robotic system capable of forward swimming motion while the same robot fitted with a plain feather is not able to move forward

Powering Artificial Enzymatic Cascades with Electrical Energy

We have developed a scalable platform that employs electrolysis for an in vitro synthetic enzymatic cascade in a continuous flow reactor. Both H2 and O2 were produced by electrolysis and transferred through a gas‐permeable membrane into the flow system. The membrane enabled the separation of the electrolyte from the biocatalysts in the flow system, where H2 and O2 served as electron mediators for the biocatalysts. We demonstrate the production of methylated N‐heterocycles from diamines with up to 99 % product formation as well as excellent regioselective labeling with stable isotopes. Our platform can be applied for a broad panel of oxidoreductases to exploit electrical energy for the synthesis of fine chemicals.DFG, 284111627, H2-basierende Kaskaden für die Biosynthese von N-HeterocyclenTU Berlin, Open-Access-Mittel – 2020DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat"DFG, 390677874, EXC 2033: RESOLV (Ruhr Explores Solvation

Bio-inspired Reflex System for Learning Visual Information for Resilient Robotic Manipulation

Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex ac- tions and also serves as a means of ‘negative reinforcement’ to allow learning of poor actions or decision. Equipping robots with a reflex system and parallel learning architecture could help to prolong their useful life and allow for continued learning of safe actions. Focusing on a specific mock-up scenario of cubes on a ‘stove’ like setup, we investigate the hardware and learning approaches for a robotic manipulator to learn the presence of ‘hot’ objects and its contextual relationship to the environment. By creating a reflex arc using analog electronics that bypasses the ‘brain’ of the system we show an increase in the speed of release by at least two-fold. In parallel we have a learning procedure which combines visual information of the scene with this ‘pain signal’ to learn and predict when an object may be hot, utilizing an object detection neural network. Finally, we are able to extract the learned contextual information of the environment by introducing a method inspired by ‘thought experiments’ to generate heatmaps that indicate the probability of the environment being hot.CREATE-LA

HTDet: A Hybrid Transformer-Based Approach for Underwater Small Object Detection

As marine observation technology develops rapidly, underwater optical image object detection is beginning to occupy an important role in many tasks, such as naval coastal defense tasks, aquaculture, etc. However, in the complex marine environment, the images captured by an optical imaging system are usually severely degraded. Therefore, how to detect objects accurately and quickly under such conditions is a critical problem that needs to be solved. In this manuscript, a novel framework for underwater object detection based on a hybrid transformer network is proposed. First, a lightweight hybrid transformer-based network is presented that can extract global contextual information. Second, a fine-grained feature pyramid network is used to overcome the issues of feeble signal disappearance. Third, the test-time-augmentation method is applied for inference without introducing additional parameters. Extensive experiments have shown that the approach we have proposed is able to detect feeble and small objects in an efficient and effective way. Furthermore, our model significantly outperforms the latest advanced detectors with respect to both the number of parameters and the mAP by a considerable margin. Specifically, our detector outperforms the baseline model by 6.3 points, and the model parameters are reduced by 28.5 M

Control and Morphology Optimization of Passive Asymmetric Structures for Robotic Swimming

Aquatic creatures exhibit remarkable adaptations of their body to efficiently interact with the surrounding fluid. The tight coupling between their morphology, motion, and the environment are highly complex but serves as a valuable example when creating biomimetic structures in soft robotic swimmers. We focus on the use of asymmetry in structures to aid thrust generation and maneuverability. Designs of structures with asymmetric profiles are explored so that we can use morphology to 'shape' the thrust generation. We propose combining simple simulation with automatic data-driven methods to explore their interactions with the fluid. The asymmetric structure with its co-optimized morphology and controller is able to produce 2.5 times the useful thrust compared to a baseline symmetric structure. Furthermore these asymmetric arms are validated on a robotic system capable of forward swimming motion while the same robot fitted with a plain feather is unable to move forward.CREATE-LA

Controlling Maneuverability of a Bio-Inspired Swimming Robot Through Morphological Transformation: Morphology Driven Control of a Swimming Robot

Biology provides many examples of how body adaption can be used to achieve a change in functionality. The feather star, an underwater crinoid that uses feather arms to locomote and feed, is one such system; it releases its arms to distract prey and vary its maneuverability to help escape predators. Using this crinoid as inspiration, we develop a robotic system that can alter its interaction with the environment by changing its morphology. We propose a robot that can actuate layers of flexible feathers and detach them at will. We first optimize the geometric and control parameters for a flexible feather using a hydrodynamic simulation followed by physical experiments. Second, we provide a theoretical framework for understanding how body change affects controllability. Third, we present a novel design of a soft swimming robot ( Figure 1 ) with the ability of changing its morphology. Using this optimized feather and theoretical framework, we demonstrate, on a robotic setup, how the detachment of feathers can be used to change the motion path while maintaining the same low-level controller

Use of remote sensing and GIS for improved natural resources management: case study from different agroecological zones of West Africa

Historical and recent aerial photograph and satellite images were analysed to study the change of land use/land cover and soil degradation in different agroecological zones of Nigeria and Benin. The sites were characterized by an expansion of farmland at the expense of forest and shrub, fallow and uncultivated land, at an increasing rate due to population growth, food demand and land scarcity. Sheet and gully erosion were the consequences of the land use intensification and have destroyed extensive areas of farmland and grazing land. Reduced agricultural and livestock production, declining revenue, as well as increased conflict from resource competition between farmers and pastoralists are expected for the future. To combat these problems, improved land use management through continuing land inventory, generating an environmental database, developing land use plans and controlling erosion through adequate soil conservation measures are recommended