By Annie Xie

In many animals, tool-use skills emerge from a combination of observational learning and experimentation. For example, by watching one another, chimpanzees can learn how to use twigs to “fish” for insects. Similarly, capuchin monkeys demonstrate the ability to wield sticks as sweeping tools to pull food closer to themselves. While one might wonder whether these are just illustrations of “monkey see, monkey do,” we believe these tool-use abilities indicate a greater level of intelligence.

By Frederik Ebert and Stephen Tian

Guiding our fingers while typing, enabling us to nimbly strike a matchstick, and inserting a key in a keyhole all rely on our sense of touch. It has been shown that the sense of touch is very important for dexterous manipulation in humans. Similarly, for many robotic manipulation tasks, vision alone may not be sufficient – often, it may be difficult to resolve subtle details such as the exact position of an edge, shear forces or surface textures at points of contact, and robotic arms and fingers can block the line of sight between a camera and its quarry. Augmenting robots with this crucial sense, however, remains a challenging task.

Our goal is to provide a framework for learning how to perform tactile servoing, which means precisely relocating an object based on tactile information. To provide our robot with tactile feedback, we utilize a custom-built tactile sensor, based on similar principles as the GelSight sensor developed at MIT. The sensor is composed of a deformable, elastomer-based gel, backlit by three colored LEDs, and provides high-resolution RGB images of contact at the gel surface. Compared to other sensors, this tactile sensor sensor naturally provides geometric information in the form of rich visual information from which attributes such as force can be inferred. Previous work using similar sensors has leveraged the this kind of tactile sensor on tasks such as learning how to grasp, improving success rates when grasping a variety of objects.

By Tijana Zrnic

“Scientific research has changed the world. Now it needs to change itself. – The Economist, 2013

There has been a growing concern about the validity of scientific findings. A multitude of journals, papers and reports have recognized the ever smaller number of replicable scientific studies. In 2016, one of the giants of scientific publishing, Nature, surveyed about 1,500 researchers across many different disciplines, asking for their stand on the status of reproducibility in their area of research. One of the many takeaways to the worrisome results of this survey is the following: 90% of the respondents agreed that there is a reproducibility crisis, and the overall top answer to boosting reproducibility was “better understanding of statistics”. Indeed, many factors contributing to the explosion of irreproducible research stem from the neglect of the fact that statistics is no longer as static as it was in the first half of the 20th century, when statistical hypothesis testing came into prominence as a theoretically rigorous proposal for making valid discoveries with high confidence.

By Rohin Shah and Dmitrii Krasheninnikov

It would be great if we could all have household robots do our chores for us. Chores are tasks that we want done to make our houses cater more to our preferences; they are a way in which we want our house to be different from the way it currently is. However, most “different” states are not very desirable:

Surely our robot wouldn’t be so dumb as to go around breaking stuff when we ask it to clean our house? Unfortunately, AI systems trained with reinforcement learning only optimize features specified in the reward function and are indifferent to anything we might’ve inadvertently left out. Generally, it is easy to get the reward wrong by forgetting to include preferences for things that should stay the same, since we are so used to having these preferences satisfied, and there are so many of them. Consider the room below, and imagine that we want a robot waiter that serves people at the dining table efficiently. We might implement this using a reward function that provides 1 reward whenever the robot serves a dish, and use discounting so that the robot is incentivized to be efficient. What could go wrong with such a reward function? How would we need to modify the reward function to take this into account? Take a minute to think about it.

Using the fossil and fossilized footprints of a 300-million-year-old animal, scientists from EPFL and Humboldt-Universität zu Berlin have identified the most likely gaits of extinct animals and designed a robot that can recreate an extinct animal’s walk. This study can help researchers better understand how vertebrate locomotion evolved over time.

People’s interactions with machines, from robots that throw tantrums when they lose a colour-matching game against a human opponent to the bionic limbs that could give us extra abilities, are not just revealing more about how our brains are wired – they are also altering them.

Emily Cross is a professor of social robotics at the University of Glasgow in Scotland who is examining the nature of human-robot relationships and what they can tell us about human cognition.

In the 21st century, governments cannot ignore how changes in technology will affect employment and political stability.

The automation of work – principally through robotics, artificial intelligence (AI) and the Internet of things (IoT), collectively known as the Fourth Industrial Revolution – will provide an unprecedented boost to productivity and profit. It will also threaten the stability of low- and mid-skilled jobs in many developing and middle-income countries.

It’s part of a field of work that is building machines that can provide real-time help using only limited data as input. Standard machine-learning algorithms often need to process thousands of possibilities before deciding on a solution, which may be impractical in pressurised scenarios where fast adaptation is critical.

Ivar Mendez, University of Saskatchewan

It is the middle of the winter and a six-month-old child is brought with acute respiratory distress to a nursing station in a remote community in the Canadian North.

By Lindsay Brownell

Jet engines can have up to 25,000 individual parts, making regular maintenance a tedious task that can take over a month per engine. Many components are located deep inside the engine and cannot be inspected without taking the machine apart, adding time and costs to maintenance. This problem is not only confined to jet engines, either; many complicated, expensive machines like construction equipment, generators, and scientific instruments require large investments of time and money to inspect and maintain.

Our work published today in Science Robotics describes how we grow fully self-organised shapes using a swarm of 300 coin-sized robots. The work was led by James Sharpe at EMBL and the Centre for Genomic Regulation (CRG) in Barcelona – together with my team at the Bristol Robotics Laboratory and University of Bristol.

A research team from the University of Zurich and EPFL has developed a new drone that can retract its propeller arms in flight and make itself small to fit through narrow gaps and holes. This is particularly useful when searching for victims of natural disasters.