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To enable sophisticated interactions between humans and robots in a shared environment, robots must infer the intentions and strategies of their human counterparts. This inference can provide a competitive edge to the robot or enhance human-robot collaboration by reducing the necessity for explicit communication about task decisions. In this work, we identify specific states within the shared environment, which we refer to as Critical Decision Points, where the actions of a human would be especially indicative of their high-level strategy. A robot can significantly reduce uncertainty regarding the human’s strategy by observing actions at these points. To demonstrate the practical value of Critical Decision Points, we propose a Receding Horizon Planning (RHP) approach for the robot to influence the movement of a human opponent in a competitive game of hide-and-seek in a partially observable setting. The human plays as the hider and the robot plays as the seeker. We show that the seeker can influence the hider to move towards Critical Decision Points, and this can facilitate a more accurate estimation of the hider’s strategy. In turn, this helps the seeker catch the hider faster than estimating the hider’s strategy whenever the hider is visible or when the seeker only optimizes for minimizing its distance to the hider. 

IntroductionComputer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. MethodsHere, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum’s size and for classifying haploid and diploid kernels. Results and discussionWe show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.