We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr^(−1) for a GWB from supermassive black hole binaries of A_(gw) < 1.5 x 10^(-15). We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, Ω_(gw)(f)h^2 < 4.2 x 10^(-10). Our limit on the cosmic string GWB, Ω_(gw)(f)h^2 < 2.2 x 10^(-10), translates to a conservative limit on the cosmic string tension with Gµ < 3.3 x 10^(-8), a factor of four better than the joint Planck and high-l cosmic microwave background data from other experiments.

/pdf/the-nanograv-nine-year-data-set-limits-on-the-isotropic-1rbv89pvud.pdf

The NANOGrav Nine-year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background

We trace the assembly history of red galaxies since z = 1 by measuring their evolving space density with the B-band luminosity function. Our sample of 39,599 red galaxies, selected from 6.96 deg2 of imaging from the NOAO Deep Wide-Field Survey and the Spitzer IRAC Shallow Survey, is an order of magnitude larger, in size and volume, than comparable samples in the literature. We measure a higher space density of z ~ 0.9 red galaxies than some of the recent literature, in part because we account for the faint yet significant galaxy flux that falls outside of our photometric aperture. The B-band luminosity density of red galaxies, which effectively measures the evolution of ~L* galaxies, increases by only 36% ± 13% from z = 0 to z = 1. If red galaxy stellar populations have faded by 1.24 B-band magnitudes since z = 1, the stellar mass contained within the red galaxy population has roughly doubled over the past 8 Gyr. This is consistent with star-forming galaxies being transformed into L* red galaxies after a decline in their star formation rates. In contrast, the evolution of 4L* red galaxies differs only slightly from a model with negligible z < 1 star formation and no galaxy mergers. If this model approximates the luminosity evolution of red galaxy stellar populations, then 80% of the stellar mass contained within today's 4L* red galaxies was already in place at z = 0.7. While red galaxy mergers have been observed, such mergers do not produce rapid growth of 4L* red galaxy stellar masses between z = 1 and the present day.

The Evolving Luminosity Function of Red Galaxies

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

Inspired by physically adaptive, agile, reconfigurable and multifunctional soft-bodied animals and human muscles, soft actuators have been developed for a variety of applications, including soft grippers, artificial muscles, wearables, haptic devices and medical devices. However, the complex performance of biological systems cannot yet be fully replicated in synthetic designs. In this Review, we discuss new materials and structural designs for the engineering of soft actuators with physical intelligence and advanced properties, such as adaptability, multimodal locomotion, self-healing and multi-responsiveness. We examine how performance can be improved and multifunctionality implemented by using programmable soft materials, and highlight important real-world applications of soft actuators. Finally, we discuss the challenges and opportunities for next-generation soft actuators, including physical intelligence, adaptability, manufacturing scalability and reproducibility, extended lifetime and end-of-life strategies. Soft actuators are flexible and compliant and thus perfectly suited to interact with the human body. This Review discusses tethered, untethered and biohybrid soft actuation strategies, highlights promising real-world applications of soft robots and identifies key future challenges, such as implementing physical intelligence and end-of-life strategies.

Soft actuators for real-world applications

Herein, the progress of machine learning methods in the field of soft robotics, specifically in the applications of sensing and control, is outlined. Data‐driven methods such as machine learning are especially suited to systems with governing functions that are unknown, impractical or impossible to represent analytically, or computationally intractable to integrate into real‐world solutions. Function approximation with careful formulation of the machine learning architecture enables the encoding of dynamic behavior and nonlinearities, with the added potential to address hysteresis and nonstationary behavior. Supervised learning and reinforcement learning in simulation and on a wide variety of physical robotic systems have shown promising results for the use of empirical data‐driven methods as a solution to contemporary soft robotics problems.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.201900171

Machine Learning for Soft Robotic Sensing and Control

This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.

House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation

House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation

We present a computational approach to creating animated plushies, soft robotic plush toys specifically-designed to reenact user-authored motions. Our design process is inspired by muscular hydrostat structures, which drive highly versatile motions in many biological systems. We begin by instrumenting simulated plush toys with a large number of small, independently-actuated, virtual muscle-fibers. Through an intuitive posing interface, users then begin animating their plushie. A novel numerical solver, reminiscent of inverse-kinematics, computes optimal contractions for each muscle-fiber such that the soft body of the plushie deforms to best match user input. By analyzing the co-activation patterns of the fibers that contribute most to the plushie's motions, our design system generates physically-realizable winch-tendon networks. Winch-tendon networks model the motorized cable-driven actuation mechanisms that drive the motions of our real-life plush toy prototypes. We demonstrate the effectiveness of our computational approach by co-designing motions and actuation systems for a variety of physically-simulated and fabricated plushies.

https://dl.acm.org/doi/pdf/10.1145/3072959.3073700

Interactive design of animated plushies

This paper proposes a generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator further allows us to improve a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects. Code, model, and data are available at https://ennauata.github.io/houseganpp/page.html.

/pdf/house-gan-generative-adversarial-layout-refinement-network-2u9jbsja4p.pdf

House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

This paper presents a series of control strategies for soft compliant manipulators. We provide a novel approach to control multi-fingered tendon-driven foam hands using a CyberGlove and a simple ridge regression model. The results achieved include complex posing, dexterous grasping and in-hand manipulations. To enable efficient data sampling and a more intuitive design process of foam robots, we implement and evaluate a finite element based simulation. The accuracy of this model is evaluated using a Vicon motion capture system. We then use this simulation to solve inverse kinematics and compare the performance of supervised learning, reinforcement learning, nearest neighbor and linear ridge regression methods in terms of their accuracy and sample efficiency.

Kai-Hung Chang

Papers

House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation

House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation

Interactive design of animated plushies

House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

Control of Tendon-Driven Soft Foam Robot Hands