Estimating the state of biological specimens is challenging due to limited observation through microscopic vision. For instance, during mouse skull drilling, the appearance alters little when thinning bone tissue because of its semi-transparent property and the high-magnification microscopic vision. To obtain the object's state, we introduce an object state estimation method for biological specimens through active interaction based on the deflection. The method is integrated to enhance the autonomous drilling system developed in our previous work. The method and integrated system were evaluated through 12 autonomous eggshell drilling experiment trials. The results show that the system achieved a 91.7% successful ratio and 75% detachable ratio, showcasing its potential applicability in more complex surgical procedures such as mouse skull craniotomy. This research paves the way for further development of autonomous robotic systems capable of estimating the object's state through active interaction.

Holacracy is an innovative management model proposed by Brian Robertson, the founder of the software company. It is a democratic and open organizational structure with shared governance for all, aiming at the decentralized management of an organization by breaking the authoritarianism of the leadership through the assumption of work by roles[1]. Such a management model is better to give employees the freedom to be more creative; however, at the same time, it also creates conflicts between roles and teams, resulting in many organizational practices ending in failure [2]. Although some static influence mechanisms have been explored in the past [3, 4], the dynamic operation of the system, like autority delegation, is not well understood. In this paper, based on the simulation capacity of Large Language Model (LLM) [5, 6], we built CareerAgent, an organizational behavior simulation framework based on LLM Agents, as shown in Figure 1, to simulate the operation of organizations under the holacracy framework, and found some interesting phenomena. One of the characteristics of the holacracy is that the leaders delegate their authority to the employees at the lower level.

Robotic assistance for experimental manipulation in the life sciences is expected to enable favorable outcomes, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability hence require intricate algorithms for successful autonomous robotic control. As a use case, we are studying the creation of cranial windows in mice. This operation requires the removal of an 8-mm-circular patch of the skull, which is approximately 300 um thick, but the shape and thickness of the mouse skull significantly varies depending on the strain of mouse, sex, and age. In this work, we propose an autonomous robotic drilling method with no offline planning, consisting of a trajectory planning block with execution-time feedback with completion level recognition based on image and force information. The force information allows for completion-level resolution to increase 10 fold. We evaluate the proposed method in two ways. First, in an eggshell drilling task and achieved a success rate of 95% and average drilling time of 7.1 min out of 20 trials. Second, in postmortem mice and with a success rate of 70% and average drilling time of 9.3 min out of 20 trials.