We propose a gripper displacement-magnification mechanism and an extendable finger mechanism, both of which can be attached to a commercially available parallel gripper. We then verify the operation of the mechanism in order to expand applications of the parallel gripper. The displacement-magnification mechanism has a stacked rack-and-pinion system that doubles displacement. The extendable finger mechanism has two nails that extend and contract, reducing impact force and detecting changes in product height from expansion and contraction amounts. The parallel gripper has a width of 95 mm, a depth of 110 mm, and a height of 214 mm and weighs 1.36 kg. It has an open/close stroke of 60 mm, a gripping force of 7.4 N, and an opening/closing speed of 100 mm/s or more. Further, it was confirmed that the ends and inclinations of products can be reliably detected using the extending/contracting nail. The mechanism verification confirmed that our parallel gripper achieved the desired performance and is therefore useful.

In this paper, we focus on a method that integrates a physical model into a neural network. This study proposes a neural network that can predict two components, namely outputs based on a physical model and its model discrepancy. To achieve such a goal, we propose a novel neural network architecture and associated loss functions designed based on a target physical model. The physical model is used as a regularizer of spatial behavior where output from the neural network is used as an intermediate variable. Then, the model discrepancy is defined as its residual to the observation value. We also propose a network architecture which has Shared and Non-Shared networks, and the neural network can be trained by alternate optimization. We constructed the proposed method with wind prediction in the upper troposphere based on thermal wind equations as an example. The experimental results demonstrate that the proposed method can achieve higher predictive accuracy than normal convolutional neural network or using thermal wind equation, also the obtained model discrepancy expresses convergence and divergence of wind vectors.

For a depalletizing robot to be practical, it has to be able to work skillfully in a limited space with a depalletizinging speed equal to or exceeding that of a human. Our new robot has the following advantages. A gantry robot and a telescopic arm mechanism are adopted so that the robot, although space-saving, has a large workspace. Using camera image and depth image, the robot recognizes the various package stacked complicatedly and decides the handling order of the package. The robot generates the most suitable handling path and performs handling control such that stacks do not collapse. The robot achieves fall prevention of the package and stable conveyance by supporting beneath the package with another arm, the conveyor arm. Moreover, in order to accelerate processing of packages, we thought it was important for the robot to be equipped with a wide end effector capable holding multiple packages simultaneously. Therefore we developed a depalletizing simulator and performed the optimal design of the end effector by using a simulator. This, we were able to realize a compact depalletizing robot with a depalletizing speed equal to that of a human.

In collaborative learning in virtual learning environments, because of the restrictions of the communication means, grasping the learning situations of others is difficult. Learning activities taken in individual spaces, such as taking notes or investigating unknown topics, are especially important for grasping the understanding situations of others. In this paper, we propose an awareness support interface that provides actions in individual spaces as awareness information. By noticing other learners’ individual activities, smooth and effective communication is promoted. Learners do not need the awareness information of all other learners all the time. They may see the actions of others who may provide effective information for them. Effective learners can be detected by the roles in the discussion. Therefore, in this paper, we propose a method for estimating the roles of learners in discussions. Then based on the estimated roles, the learner’s focus for acquiring actions in individual spaces is detected.