In the field of robotic manipulation, deep imitation learning is recognized as a promising approach for acquiring manipulation skills. Additionally, learning from diverse robot datasets is considered a viable method to achieve versatility and adaptability. In such research, by learning various tasks, robots achieved generality across multiple objects. However, such multi-task robot datasets have mainly focused on single-arm tasks that are relatively imprecise, not addressing the fine-grained object manipulation that robots are expected to perform in the real world. This paper introduces a dataset of diverse object manipulations that includes dual-arm tasks and/or tasks requiring fine manipulation. To this end, we have generated dataset with 224k episodes (150 hours, 1,104 language instructions) which includes dual-arm fine tasks such as bowl-moving, pencil-case opening or banana-peeling, and this data is publicly available. Additionally, this dataset includes visual attention signals as well as dual-action labels, a signal that separates actions into a robust reaching trajectory and precise interaction with objects, and language instructions to achieve robust and precise object manipulation. We applied the dataset to our Dual-Action and Attention (DAA), a model designed for fine-grained dual arm manipulation tasks and robust against covariate shifts. The model was tested with over 7k total trials in real robot manipulation tasks, demonstrating its capability in fine manipulation. The dataset is available at https://sites.google.com/view/multi-task-fine.

The engagement of employees at the workplace is one of the main ingredients for company growth. Therefore, the motivational systems that encourage engagement in the staff can significantly boost the realization of development aids. With the births ranging from the late 1990s till 2010s, the persons from Generation Z started or soon will start their first jobs in companies. High productivity of employees from this generation can be achieved by crafting a proper motivation system. Such a system must also be designed to tie the employee with the company since otherwise, the experience will be lost during the work rotation.

Facing this tsunami of transformation is a tall task, but the spirit of Davos is to synthesize the global noise--the voices, viewpoints and vision of the government leaders, academic experts, corporate executives and individuals who must collaborate on solutions for the future. Optimistically, writes Klaus Schwab, "a new framework for global public-private cooperation has been taking shape. Public-private cooperation is about harnessing the private sector and open markets to drive economic growth for the public good, with environmental sustainability and social inclusiveness always in mind."