Redwing, a robotic submarine about the size of a surfboard, is embarking on a five-year journey that will follow the famed explorer Ferdinand Magellan's voyage around the world A small robot submarine is setting out to go around the world for the first time. Teledyne Marine and Rutgers University New Brunswick in New Jersey are launching an underwater glider called Redwing on its Sentinel Mission from Martha's Vineyard in Massachusetts on 11 October. Researchers have been using underwater gliders since the 1990s. Rather than a propeller, gliders have a buoyancy engine, a gas-filled piston that slightly changes the craft's overall buoyancy. An electric motor pushes the piston in to make the glider heavier than water so it slowly sinks, coasting downwards at a shallow angle.

A huge, super-fast indestructible robot hand might seem like a terrifying prop from a science-fiction film. But this hefty 4.1kg (9.9lbs) hand is very real and is already being used to develop the next generation of AI robots. Designed by UK-based Shadow Robot Company, this three-fingered claw can go from fully open to closed in just 500 milliseconds. However, the robot hand is still tough enough to resist being bashed with hammers or pounded by pistons. That toughness is designed to help the hand survive the rigorous and often destructive process of teaching AI how to interact with the world.

Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.

Robotic legs have bimodal operations: swing phases when the leg needs to move quickly in the air (high-speed, low-force) and stance phases when the leg bears the weight of the system (low-speed, high-force). Sizing a traditional single-ratio actuation system for such extremum operations leads to oversized heavy electric motor and poor energy efficiency, which hinder the capability of legged systems that bear the mass of their actuators and energy source. This paper explores an actuation concept where a hydrostatic transmission is dynamically reconfigured using valves to suit the requirements of each phase of a robotic leg. An analysis of the mass-delay-flow trade-off for the switching valve is presented. Then, a custom actuation system is built and integrated on a robotic leg test bench to evaluate the concept. Experimental results show that 1) small motorized ball valves can make fast transitions between operating modes when designed for this task, 2) the proposed operating principle and control schemes allow for seamless transitions, even during an impact with the ground and 3) the actuator characteristics address the needs of a leg bimodal operation in terms of force, speed and compliance.

Soft robots are promising for manipulation tasks thanks to their compliance, safety, and high degree of freedom. However, the commonly used bidirectional continuum segment design means soft robotic manipulators only function in a limited hemispherical workspace. This work increases a soft robotic arm's workspace by designing, fabricating, and controlling an additional soft prismatic actuator at the base of the soft arm. This actuator consists of pneumatic artificial muscles and a piston, making the actuator back-driveable. We increase the task space volume by 116\%, and we are now able to perform manipulation tasks that were previously impossible for soft robots, such as picking and placing objects at different positions on a surface and grabbing an object out of a container. By combining a soft robotic arm with a prismatic joint, we greatly increase the usability of soft robots for object manipulation. This work promotes the use of integrated and modular soft robotic systems for practical manipulation applications in human-centered environments.

Microscopic robots in the body could harvest energy from ultrasound to provide on-board control of autonomous behaviors such as measuring and communicating diagnostic information and precisely delivering drugs. This paper evaluates the acoustic power available to micron-size robots that collect energy using pistons. Acoustic attenuation and viscous drag on the pistons are the major limitations on the available power. Frequencies around 100kHz can deliver hundreds of picowatts to a robot in low-attenuation tissue within about 10cm of transducers on the skin, but much less in high-attenuation tissue such as a lung. However, applications of microscopic robots could involve such large numbers that the robots significantly increase attenuation, thereby reducing power for robots deep in the body. This paper describes how robots can collectively manage where and when they harvest energy to mitigate this attenuation so that a swarm of a few hundred billion robots can provide tens of picowatts to each robot, on average.

This tutorial provides a simple introduction to using multi-agent reinforcement learning, assuming a little experience in machine learning and knowledge of Python. Reinforcement stems from using machine learning to optimally control an agent in an environment. It works by learning a policy, a function that maps an observation obtained from its environment to an action. Policy functions are typically deep neural networks, which gives rise to the name "deep reinforcement learning." The goal of reinforcement learning is to learn an optimal policy, a policy that achieves the maximum expected reward from the environment when acting.

Machine learning algorithms are now synonymous with finding patterns in data but not all patterns are suitable for statistics based data-driven techniques, for example when these patterns don't have explicitly labelled targets to learn from. In some cases, these patterns can be expressed precisely as a rule. Reasoning is the process of matching rule-based patterns or verifying that they don't exist in a graph. Because these patterns are found with deductive logic they can be found more efficiently and interpreted more easily than Machine Learning patterns which are induced from the data. This article will introduce some common patterns and how you can express them in the rule language, Datalog, using RDFox, a knowledge graph and semantic reasoning engine developed by Oxford Semantic Technologies.

Heat pumps are already incredibly efficient. Researchers in Switzerland say they can push efficiencies even further using artificial intelligence. A research team led by Jürg Alexander Schiffmann at the L'Ecole Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne, or EPFL) is using AI to design compressors that slash heat pumps' electricity consumption by around 25 percent. Unlike conventional furnaces or boilers, which combust fuels to generate heat, heat pumps use electricity to move heat from one place to another. Employing a compressor and refrigerant, heat pumps expel heat from the indoors to the outside during the cooling season, or capture heat outdoors from the ground or air and draw it indoors in winter.

Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).