Abstract-- Multilayer piezoelectric polyvinylidene fluoride (PVDF) actuators are a promising approach to enhance performance of soft microrobotic systems. In this work, we develop and characterize multilayer PVDF actuators with parallel voltage distribution across each layer, bridging a unique design space between brittle high-force PZT stacks and compliant but lower-bandwidth soft polymer actuators. We show the effects of layer thickness and number of layers in actuator performance and their agreement with a first principles model. By varying these parameters, we demonstrate actuators capable of >3 mm of free deflection, >20 mN of blocked force, and >=500 Hz, while operating at voltages as low as 150 volts. T o illustrate their potential for robotic integration, we integrate our actuators into a planar, translating microrobot that leverages resonance to achieve locomotion with robustness to large perturbations.

Sensory substitution is an effective approach for displaying stable haptic feedback to a teleoperator under time delay. The finger is highly articulated, and can sense movement and force in many directions, making it a promising location for sensory substitution based on kinesthetic feedback. However, existing finger kinesthetic devices either provide only one-degree-of-freedom feedback, are bulky, or have low force output. Soft pneumatic actuators have high power density, making them suitable for realizing high force kinesthetic feedback in a compact form factor. We present a soft pneumatic handheld kinesthetic feedback device for the index finger that is controlled using a constant curvature kinematic model. \changed{It has respective position and force ranges of +-3.18mm and +-1.00N laterally, and +-4.89mm and +-6.01N vertically, indicating its high power density and compactness. The average open-loop radial position and force accuracy of the kinematic model are 0.72mm and 0.34N.} Its 3Hz bandwidth makes it suitable for moderate speed haptic interactions in soft environments. We demonstrate the three-dimensional kinesthetic force feedback capability of our device for sensory substitution at the index figure in a virtual telemanipulation scenario.

Plants display physical displacements during their growth due to photosynthesis, which converts light into chemical energy. This can be interpreted as plants acting as actuators with a built-in power source. This paper presents a method to create plant robots that move and perform tasks by harnessing the actuation output of plants: displacement and force generated from the growing process. As the target plant, radish sprouts are employed, and their displacement and force are characterized, followed by the calculation of power and energy densities. Based on the characterization, two different plant robots are designed and fabricated: a rotational robot and a gripper. The former demonstrates ground locomotion, achieving a travel distance of 14.6 mm with an average speed of 0.8 mm/h. The latter demonstrates the picking and placing of an object with a 0.1-g mass by the light-controlled open-close motion of plant fingers. A good agreement between the experimental and model values is observed in the specific data of the mobile robot, suggesting that obtaining the actuation characteristics of plants can enable the design and prediction of behavior in plant robots. These results pave the way for the realization of novel types of environmentally friendly and sustainable robots.

3D spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely-largescale-programable-metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the Desired Focal Point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSIindependent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the Phase Distribution Image of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing Quasi-Liquid-Layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

The rapid growth of data generation fueled by artificial intelligence (AI) has transformed how data is stored, processed, managed, and transferred, while increasing the demand for computing power across cloud and edge data centers. To meet the demand generated by AI, data centers are evolving and adapting their design, power infrastructure, and cooling equipment in various unique ways. Data centers provide vast computing resources and storage, enabling artificial intelligence (AI) to process massive datasets for training and inference. By hosting specialized hardware such as GPUs and TPUs, data centers accelerate complex calculations, supporting AI applications and workloads. As Dgtl Infra delves deeper into the evolving relationship between artificial intelligence and data centers, we offer insights on power consumption, cooling requirements, and the pivotal role of data centers in supporting AI.

A squishy robot smaller than a postage stamp can run 70 of its body lengths every second – more than three times faster than a cheetah, relative to its body size. "It is really, really fast and, to be honest, that was a little bit of a surprise," says Martin Kaltenbrunner at Johannes Kepler University Linz in Austria. "We actually bought a better version of a high-speed camera during the experiment because the one we had wasn't good enough." He and his colleagues made the ultra-fast soft robot out of a rubbery material and controlled it with electric currents and a magnetic field. They hope it will eventually be used in medicine, for delivering drugs or performing procedures inside the human body. The robot is made of an elastic material curled into an upside-down U-shape with embedded metal wires running through it.

What if the brain is highly efficient? Brain efficiency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity. If the brain is about 6 OOM away from the practical physical limits of energy efficiency, then roughly speaking we should expect about 6 OOM of further Moore's Law hardware improvement past the point of brain parity: perhaps two decades of progress at current rates, which could be compressed into a much shorter time period by an intelligence explosion - a hard takeoff. But if the brain is already near said practical physical limits, then merely achieving brain parity in AGI at all will already require using up most of the optimizational slack, leaving not much left for a hard takeoff - thus a slower takeoff. In worlds where brains are efficient, AGI is first feasible only near the end of Moore's Law (for non-exotic, reversible computers), whereas in worlds where brains are highly inefficient, AGI's arrival is more decorrelated, but would probably come well before any Moore's Law slowdown. In worlds where brains are ultra-efficient, AGI necessarily becomes neuromorphic or brain-like, as brains are then simply what economically efficient intelligence looks like in practice, as constrained by physics. This has important implications for AI-safety: it predicts/postdicts the success of AI approaches based on brain reverse engineering (such as DL) and the failure of non-brain like approaches, it predicts that AGI will consume compute & data in predictable brain like ways, and it suggests that AGI will be far more like human simulations/emulations than you'd otherwise expect and will require training/education/raising vaguely like humans, and thus that neuroscience and psychology are perhaps more useful for AI safety than abstract philosophy and mathematics. If we live in such a world where brains are highly efficient, those of us interested in creating benevolent AGI should immediately drop everything and learn how brains work. Computation is an organization of energy in the form of ordered state transitions transforming physical information towards some end. Computation requires an isolation of the computational system and its stored information from the complex noisy external environment. If state bits inside the computational system are unintentionally affected by the external environment, we call those bit errors due to noise, errors which must be prevented by significant noise barriers and or potentially costly error correction techniques.

Colocation giant Digital Realty deepened its ties to Nvidia with a service that allows enterprises to deploy Nvidia-powered artificial intelligence (AI) and machine learning workloads on Digital Realty's data center platform. Nvidia launched its DGX-Ready Data Center program last year with 19 data center partners including Digital Realty. The AI partner program gives customers access to Nvidia's AI infrastructure inside the colocation providers' facilities. Meanwhile, Digital Realty in November announced PlatformDigital. At launch the data center platform offered customers four new services that they could deploy on top of PlatformDigital.

At the IEEE International Electron Devices Meeting (IEDM) in San Francisco this week, France-based research institute CEA-Leti presented papers highlighting its achievements in bio-inspired neural networks, a readout technique for high-fidelity measurements in large quantum dot arrays and inorganic thin film batteries with optimum energy and power density performance for medical and implantable devices. This article presents highlights of each of these three papers. Bio-inspired neural networks have been in development for a while, and at IEDM, Leti announced it had fabricated a fully integrated bio-inspired neural network, combining resistive-RAM-based synapses and analog spiking neurons. The functionality of this proof-of-concept circuit was demonstrated thanks to handwritten digits classification. "The entire network is integrated on-chip," said Alexandre Valentian, lead author of the paper, Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses.

The sight of a RoboBee careening towards a wall or crashing into a glass box may have once triggered panic in the researchers in the Harvard Microrobotics Laboratory at the Harvard John A. Paulson School of Engineering and Applied Science (SEAS), but no more. Researchers at SEAS and Harvard's Wyss Institute for Biologically Inspired Engineering have developed a resilient RoboBee powered by soft artificial muscles that can crash into walls, fall onto the floor, and collide with other RoboBees without being damaged. It is the first microrobot powered by soft actuators to achieve controlled flight. "There has been a big push in the field of microrobotics to make mobile robots out of soft actuators because they are so resilient," said Yufeng Chen, Ph.D., a former graduate student and postdoctoral fellow at SEAS and first author of the paper. "However, many people in the field have been skeptical that they could be used for flying robots because the power density of those actuators simply hasn't been high enough and they are notoriously difficult to control. Our actuator has high enough power density and controllability to achieve hovering flight."