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This paper aims to automate the picking task needed in robotic assembly. Parts supplied to an assembly process are usually randomly staked in a box. If randomized bin-picking is introduced to a production process, we do not need any part-feeding machines or human workers to once arrange the objects to be picked by a robot. The authors introduce a learning-based method for randomized bin-picking.

The authors combine the learning-based approach on randomized bin-picking (Harada et al., 2014b) with iterative visual recognition (Harada et al., 2016a) and show additional experimental results. For learning, we use random forest explicitly considering the contact between a finger and a neighboring object. The iterative visual recognition method iteratively captures point cloud to obtain more complete point cloud of piled object by using 3D depth sensor attached at the wrist.

Compared with the authors’ previous research (Harada et al., 2014b) (Harada et al., 2016a), their new finding is as follows: by using random forest, the number of training data becomes extremely small. By adding penalty to occluded area, the learning-based method predicts the success after point cloud with less occluded area. We analyze the calculation time of the iterative visual recognition. We furthermore make clear the cases where a finger contacts neighboring objects.

The originality exists in the part where the authors combined the learning-based approach with the iterative visual recognition and supplied additional experimental results. After obtaining the complete point cloud of the piled object, prediction becomes effective.

Following the job demands-resources theory, this study aims to investigate the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources.

Based on a large-scale job stress survey of approximately 96,000 employee-year observations ranging from 2017 to 2019, this study applies structural equation modeling to construct latent workplace resources at the task, group and worksite levels and then examines the impact of female managers on employee well-being, including occupational stress, job satisfaction, work engagement and workplace cohesiveness.

The findings provide supporting evidence for the transformational leadership behaviors of female managers. The presence of women in management is associated with improved workplace resources and employee well-being, particularly workplace cohesiveness, work engagement and reduced occupational stress. These relationships are significantly mediated by workplace resources, which elucidates the underlying mechanisms involved. Notably, the positive indirect effects via workplace resources could counteract the negative direct effects of female managers. Compared with top managers, female middle managers have more substantial impacts.

In practice, it is recommended to promote female representation at the management level and strengthen policies that support female middle managers to ensure favorable effects on workplace resources. In a gender-diverse management team, it is important to share female managers’ experiences in improving employee psychological well-being.

This study provides new empirical evidence to support the transformational leadership behaviors of female managers and elucidates the mechanism of female managers’ influence on employee well-being by introducing workplace resources as mediators.