Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network

To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of tasks performed by them increase. To handle the switching and combination of multiple behaviors, we propose a method to design dynamical systems based on point attractors that accept (i) "instruction signals" for instruction-driven switching. We incorporate the (ii) "instruction phase" to form a point attractor and divide the target task into multiple subtasks. By forming an instruction phase that consists of point attractors, the model embeds a subtask in the form of trajectory dynamics that can be manipulated using sensory and instruction signals. Our model comprises two deep neural networks: a convolutional autoencoder and a multiple time-scale recurrent neural network. In this study, we apply the proposed method to manipulate soft materials. To evaluate our model, we design a cloth-folding task that consists of four subtasks and three patterns of instruction signals, which indicate the direction of motion. The results depict that the robot can perform the required task by combining subtasks based on sensory and instruction signals. And, our model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied video is available at this https://youtu.be/a73KFtOOB5

Hadronic Paschen-Back effect

We find a novel phenomenon induced by the interplay between a strong magnetic field and finite orbital angular momenta in hadronic systems, which is analogous to the Paschen-Back effect observed in the field of atomic physics. This effect allows the wave functions to drastically deform. We discuss anisotropic decay from the deformation as a possibility to measure the strength of the magnetic field in heavy-ion collision at LHC, RHIC and SPS, which has not experimentally been measured. As an example we investigate charmonia with a finite orbital angular momentum in a strong magnetic field. We calculate the mass spectra and mixing rates. To obtain anisotropic wave functions, we apply the cylindrical Gaussian expansion method, where the Gaussian bases to expand the wave functions have different widths along transverse and longitudinal directions in the cylindrical coordinate.Comment: 8 pages, 8 figures, v3: updated to the published style on PL

Shape transformations of lipid vesicles by insertion of bulky-head lipids

Lipid vesicles, in particular Giant Unilamellar Vesicles (GUVs), have been increasingly important as compartments of artificial cells to reconstruct living cell-like systems in a bottom-up fashion. Here, we report shape transformations of lipid vesicles induced by polyethylene glycol-lipid conjugate (PEG lipids). Statistical analysis of deformed vesicle shapes revealed that shapes vesicles tend to deform into depended on the concentration of the PEG lipids. When compared with theoretically simulated vesicle shapes, those shapes were found to be more energetically favorable, with lower membrane bending energies than other shapes. This result suggests that the vesicle shape transformations can be controlled by externally added membrane molecules, which can serve as a potential method to control the replications of artificial cells

Effective theory of Black Holes in the 1/D expansion

The gravitational field of a black hole is strongly localized near its horizon when the number of dimensions D is very large. In this limit, we can effectively replace the black hole with a surface in a background geometry (eg Minkowski or Anti-deSitter space). The Einstein equations determine the effective equations that this 'black hole surface' (or membrane) must satisfy. We obtain them up to next-to-leading order in 1/D for static black holes of the Einstein-(A)dS theory. To leading order, and also to next order in Minkowski backgrounds, the equations of the effective theory are the same as soap-film equations, possibly up to a redshift factor. In particular, the Schwarzschild black hole is recovered as a spherical soap bubble. Less trivially, we find solutions for 'black droplets', ie black holes localized at the boundary of AdS, and for non-uniform black strings.Comment: 32 pages, 3 figure

Liposome-based liquid handling platform featuring addition, mixing, and aliquoting of femtoliter volumes

This paper describes the utilization of giant unilamellar vesicles (GUVs) as a platform for handling chemical and biochemical reagents. GUVs with diameters of 5 to 10 µm and containing chemical/biochemical reagents together with inert polymers were fused with electric pulses (electrofusion). After reagent mixing, the fused GUVs spontaneously deformed to a budding shape, separating the mixed solution into sub-volumes. We utilized a microfluidic channel and optical tweezers to select GUVs of interest, bring them into contact, and fuse them together to mix and aliquot the reaction product. We also show that, by lowering the ambient temperature close to the phase transition temperature Tm of the lipid used, daughter GUVs completely detached (fission). This process performs all the liquid-handing features used in bench-top biochemistry using the GUV, which could be advantageous for the membrane-related biochemical assays

Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask

In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). However, when applying DRL to tasks in a real-world environment, designing an appropriate reward is difficult. Rewards obtained via actual hardware sensors may include noise, misinterpretation, or failed observations. The learning instability caused by these unstable signals is a problem that remains to be solved in DRL. In this work, we propose an approach that extends existing DRL models by adding a subtask to directly estimate the variance contained in the reward signal. The model then takes the feature map learned by the subtask in a critic network and sends it to the actor network. This enables stable learning that is robust to the effects of potential noise. The results of experiments in the Atari game domain with unstable reward signals show that our method stabilizes training convergence. We also discuss the extensibility of the model by visualizing feature maps. This approach has the potential to make DRL more practical for use in noisy, real-world scenarios.Comment: Published as a conference paper at ICONIP 202