MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Ground-state properties of neutron-rich Mg isotopes

We analyze recently-measured total reaction cross sections for 24-38Mg isotopes incident on 12C targets at 240 MeV/nucleon by using the folding model and antisymmetrized molecular dynamics(AMD). The folding model well reproduces the measured reaction cross sections, when the projectile densities are evaluated by the deformed Woods-Saxon (def-WS) model with AMD deformation. Matter radii of 24-38Mg are then deduced from the measured reaction cross sections by fine-tuning the parameters of the def-WS model. The deduced matter radii are largely enhanced by nuclear deformation. Fully-microscopic AMD calculations with no free parameter well reproduce the deduced matter radii for 24-36Mg, but still considerably underestimate them for 37,38Mg. The large matter radii suggest that 37,38Mg are candidates for deformed halo nucleus. AMD also reproduces other existing measured ground-state properties (spin-parity, total binding energy, and one-neutron separation energy) of Mg isotopes. Neutron-number (N) dependence of deformation parameter is predicted by AMD. Large deformation is seen from 31Mg with N = 19 to a drip-line nucleus 40Mg with N = 28, indicating that both the N = 20 and 28 magicities disappear. N dependence of neutron skin thickness is also predicted by AMD.Comment: 15 pages, 13 figures, to be published in Phys. Rev.

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due to the lack of effective therapy coverage for mental disorders, there is a need for an AI solution that can assist therapists with the diagnosis. However, current Neural Network models lack explainability and may not be trusted by therapists. The LNN is a Recurrent Neural Network architecture that combines the learning capabilities of neural networks with the reasoning capabilities of classical logic-based AI. The proposed system uses input predicates from clinical interviews to output a mental disorder class, and different predicate pruning techniques are used to achieve scalability and higher scores. In addition, we provide an insight extraction method to aid therapists with their diagnosis. The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.Comment: ACL 202

Update of the Spectro-polarimeter on the Domeless Solar Telescope at Hida Observatory

A new spectro-polarimeter was installed on the Domeless Solar Telescope at Hida Observatory. Major update s from the previous system are as follows; a super achromatic dual beam polarimeter, communication interface between telescope and data acquisition PC for image scanning , flexible setup for observing a wavelength range of 500-1100 nm, and a high sensitivity infrared camera. The field of view of the system is 120arcsec along the slit of spectrograph, and with a slit width of 0.64 arcsec, the system can achieve a sensitivity of 3x10⁻⁴ in a few second in on disk observation. Details of the instruments, the data reduction flow and initial results obtained by the new system are presented. At the end, future prospect is also discussed