This paper addresses the challenge of co-designing morphology and control in soft robots via a novel neural network evolution approach. We propose an innovative method to implicitly dual-encode soft robots, thus facilitating the simultaneous design of morphology and control. Additionally, we introduce the large language model to serve as the control center during the evolutionary process. This advancement considerably optimizes the evolution speed compared to traditional soft-bodied robot co-design methods. Further complementing our work is the implementation of Gaussian positional encoding - an approach that augments the neural network's comprehension of robot morphology. Our paper offers a new perspective on soft robot design, illustrating substantial improvements in efficiency and comprehension during the design and evolutionary process.

Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors. It has been suggested that a major hurdle currently limiting soft robot brain-body co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls, resulting in premature convergence. Here we posit that modular controllers are more robust to changes to a robot's body plan. We demonstrate a decreased reduction in locomotion performance after morphological mutations to soft robots with modular controllers, relative to those with similar global controllers - leading to fitter offspring. Moreover, we show that the increased transferability of modular controllers to similar body plans enables more effective brain-body co-optimization of soft robots, resulting in an increased rate of positive morphological mutations and higher overall performance of evolved robots. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.

The ability of robots to model themselves without being assisted by engineers is important for many reasons: Not only does it save labor, but it also allows the robot to keep up with its own wear-and-tear, and even detect and compensate for damage. The authors argue that this ability is important as we need autonomous systems to be more self-reliant. A factory robot, for instance, could detect that something isn't moving right, and compensate or call for assistance. "We humans clearly have a notion of self," explained the study's first author Boyuan Chen, who led the work and is now an assistant professor at Duke University. "Close your eyes and try to imagine how your own body would move if you were to take some action, such as stretch your arms forward or take a step backward. Somewhere inside our brain we have a notion of self, a self-model that informs us what volume of our immediate surroundings we occupy, and how that volume changes as we move."

For decades we've been warned that artificial intelligence is coming for our jobs. Sci-fi books and movies going all the way back to Kurt Vonnegut's Player Piano portray a world where workers have been replaced by machines (or in some instances, just one machine). More recently, these ideas have moved from the annals of novels into the predictive economic papers of governments and consulting firms. In 2016, the Obama administration authored a report warning that the robots were coming, and that millions of Americans could soon be out of a job. In 2021, McKinsey predicted that algorithms and androids would vaporize 45 million jobs by 2030.

Voice technology is one of the biggest trends in the healthcare space. We look at how it might help care providers and patients, from a woman who is losing her speech, to documenting healthcare records for doctors. But how do you teach AI to learn to communicate more like a human, and will it lead to more efficient machines? This episode was reported and produced by Anthony Green with help from Jennifer Strong and Emma Cillekens. It was edited by Michael Reilly. Our mix engineer is Garret Lang and our theme music is by Jacob Gorski. Jennifer: Healthcare looks a little different than it did not so long ago…when your doctor likely wrote down details about your condition on a piece of paper...

Can they design other robots and self-repair? Why should we evolve robots to do tasks that animals do so well? Why don't we have useful autonomous robots in the real world yet? Find out Hod's answers to these questions and updates on VoxCAD development for designing and simulation of soft robots in this episode of the IEEE RAS Soft Robotics Podcast. What's more, Hod gave his personal advice to roboticists being interviewed for an assistant professorship and to 1st-year robotics PhD students looking for a thesis topic, and he also commented on his approach to the ethical dilemma of military funding scientific research.

Artificial intelligence--the ability of a computer program to perform human tasks such as thinking and learning, sometimes referred to as machine learning--is changing classrooms in both K12 and higher ed. But robotics has some questioning whether AI is just a fad that will eventually fade into obscurity or alter teaching and learning processes as we know it. Experts discussed the topic at a recent conference for future K12 educators held by the Teachers College at Columbia University, "Where Does Artificial Intelligence Fit in the Classroom?" Borhene Chakroun, director of the division for policies and lifelong learning systems at UNESCO, kicked off the event extolling the future of AI technology and its potential to "profoundly alter every aspect of the teaching and learning process." He also acknowledged the implications of AI and how it is altering how machines and humans work together.

Figure 1: A single robot grows calluses as it walks, in response to pressure on its feet (youtu.be/0cmwpcxSUWI). Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Everyone is talking about driverless cars ... After reading this book, you will be knowledgeable enough to make your own informed opinion. Driverless vehicles are poised to usher in a massive disruption of our transportation system, our urban landscapes, our economy -- and quite possibly the very fabric of society. Anyone who wants to understand what's coming must read this fascinating book. Driverless is a great read for anybody interested in technological, societal, and ethical implications of self-driving cars. The book reaches across fields and issues thoughtfully, and presents a comprehensive view of the state of the art.

Visit Singularity Hub for the latest from the frontiers of manufacturing and technology as we bring you coverage of Singularity University's Exponential Manufacturing conference. If you've been staying on top of artificial intelligence news lately, you may know that the games of chess and Go were two of the grand challenges for AI. But do you know what the equivalent is for robotics? Just think about how the game requires razor sharp perception and movement, a tall order for a machine. As entertaining as human vs. robot games can be, what they actually demonstrate is much more important.