Evaluation of Fracture Toughness for Wood-Epoxy Adhesive System under External Shear Force

この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されました。Fracture Toughness G_c of Wood-Epoxy adhesive system under external shear force was evaluated by employing the experimental compliance method based on the Griffith-Irwin fracture theory. Invariability of G_c with the different glue line length was tolerably recognized and the representative value of G_c for the above system was about 0.25 (cm・kg/cm^2) throughout the series of glue line thickness tested. Fracture mode and stress distribution were discussed with some helps of Finite Element Method

Dry-heat Degradation of Plywood-type Joint

この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されました。In order to predict the service life of plywood heating floors, dry-heat exposure test of lauan plywood made with each of six type of typical wood adhesives was carried out at 50°, 70°, 100° and 150℃ for 250 days. Internal bond strength and location of failure of the exposed specimens were measured at selected intervals. Data were plotted as a function of the exposed time. Confidence limit was applied on the regression and the service life was calculated by extraporating the lower confidence limit curve to the half-strength. The apparent mechanism of dry-heat degradation of plywood in 150° and 100℃ seemed to differ from the lower temperature exposures. On 70° and 50℃, more conservative estimation was made by the kinetic method on the Arrhenius relationship. The result showed the detrimental effect of the acid catalyst on polyvinyl-acetate cross-linked and urea resin adhesives. Plywood specimens produced with melamine, phenol, phenol-resorcinol, and resorcinol resin adhesives showed excellent durability in dry-heat exposure at temperature normally found in service of floor heating

Duality Cascades and Parallelotopes

Duality cascades are a series of duality transformations in field theories, which can be realized as the Hanany-Witten transitions in brane configurations on a circle. In the setup of the ABJM theory and its generalizations, from the physical requirement that duality cascades always end and the final destination depends only on the initial brane configuration, we propose that the fundamental domain of supersymmetric brane configurations in duality cascades can tile the whole parameter space of relative ranks by translations, hence is a parallelotope. We provide our arguments for the proposal.Comment: 37 pages, 9 eps figures; v2: section 2.2 added, four figures adde

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible non-parametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.Comment: 7 pages, Accepted by the 2021 International Conference on Robotics and Automation (ICRA 2021

Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation

Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterisation of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.Comment: 69 pages, 9 figures, accepted by Elsevier Neural Networks - Journa