Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semiautonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (Dexterous Surgical Skill) dataset. It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset was used to test the idea of transferring knowledge across different domains (e.g. from Taurus to YuMi robot) for a surgical gesture classification task with seven gestures. We explored three different scenarios: 1) No transfer, 2) Transfer from simulated Taurus to real Taurus and 3) Transfer from Simulated Taurus to the YuMi robot. We conducted extensive experiments with three supervised learning models and provided baselines in each of these scenarios. Results show that using simulation data during training enhances the performance on the real robot where limited real data is available. In particular, we obtained an accuracy of 55% on the real Taurus data using a model that is trained only on the simulator data. Furthermore, we achieved an accuracy improvement of 34% when 3% of the real data is added into the training process.

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Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations. The learnt task is corrected or extended to meet specific task goals by means of coaching. Adopting a similar framework for teaching robots through demonstrations and coaching makes teaching tasks highly intuitive. Unlike traditional Learning from Demonstration (LfD) approaches which require multiple demonstrations, we present a one-shot learning from demonstration approach to learn tasks. The learnt task is corrected and generalized using two layers of evaluation/modification. First, the robot self-evaluates its performance and corrects the performance to be closer to the demonstrated task. Then, coaching is used as a means to extend the policy learnt to be adaptable to varying task goals. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. Coaching is achieved through human feedback on desired goal and action modification to generalize to specified task goals. The proposed approach is evaluated with a scooping task, by presenting a single demonstration. The self-evaluation framework aims to reduce the resistance to scooping in the media. To reduce the search space for RL, we bootstrap the search using least resistance path obtained using resistive force theory. Coaching is used to generalize the learnt task policy to transfer the desired quantity of material. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching.

The Earth is at a sociotechnical crossroads with humanity hanging in the balance – and high-tech agriculture can help bail us out. Human population growth, increasing urbanization and rising incomes is likely to drastically increase demand for animal agriculture in the coming decades. The US Department of Agriculture (USDA) predicts the need to double global food production by 2050 as the global population increases from 7.3 billion in 2015 to 9.7 billion in 2050 as shown in Fig 1. Much of this growth will be concentrated in the world’s poorest countries where standards of living are set to rise rapidly, increasing the demand for resource-intensive meat and dairy products which has been the historical trend. At the same time, agriculture in the 21st century faces multiple challenges: it must produce more food and fiber to feed a growing population with a smaller rural labor force, produce additional feedstocks for a potentially huge bioenergy market, contribute to overall development in the many agriculture-dependent developing countries, adopt more efficient and sustainable production methods, and adapt to climate change. Additionally, the world’s arable land is already fully employed and shrinking -- the world has lost a third of its arable land due to erosion or pollution in the past 40 years. All these factors put enormous pressure on improving the production efficiency of the world’s supply of food to meet the demand.

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