Amazon Robotics Moves Closer to Displacing Human Workers

A recent video published on Amazon’s science blog features a new “pinch-grasping” robot system that has the potential to one day do a lot of the work that humans in Amazon warehouses do today. Or, at least potentially, help workers do their jobs more efficiently. Today, vacuum suction is the default technology for picking up and moving smaller objects in logistics operations.

The topic of automation has become more relevant than ever in the retail and e-commerce industries. Amazon, the second largest private employer in the US, has conducted research suggesting that they could run out of workers to hire in the US by 2024 if it did not execute a series of sweeping changes, including increasing automation in its warehouses. At the same time, it’s facing the prospect of US workers starting to unionise after a victory by the Amazon Labour Union in Staten Island and another upcoming union election in October in Upstate New York. Labour activists have long speculated that Amazon might ramp up automation efforts in response to unionisation activity.

Amazon continues to launch new machines in warehouses. In June, the company unveiled a package ferrying machine called Proteus, which it referred to as its first fully autonomous mobile robot (AMR). It has also deployed other robots that can help sort and move packages. It has been building out these technologies both organically and inorganically. Amazon’s latest acquisition in the space is privately owned Cloostermans, a Belgium company with around 200 employees, which it bought in early September.

Latest IFR Stats Show Strong Post-Pandemic Robotic Demand in 2021

The International Federation of Robotics (IFR) published the ‘World Robotics Report 2022’ in Oct. The report shows that global annual installations of industrial robots hit an all-time high at 517,385 units in 2021, beating the previous high set in 2018 by 22% and this also represents a year-over-year growth rate of 31% against 2020 which was obviously ravaged by Covid. As a result, at the end of 2021, a record 3.5 million industrial robots were operating day and night around the globe.

The IFR pointed out that the speed of annual robot installations has more than doubled within six years. Regarding geography, China is undergoing robotization and remains the primary growth driver of the sector. It saw its installed base jump by over 50% in 2021. To fight against labour shortage issues, Japan, Europe and America have all scrambled to install more industrial robots, as well and these regions saw their installed base rise by 22-31% in the same period.

The energy crisis, cost inflation and shortage of labour and electronic components have posed many challenges to the global manufacturing industry and economy over the last few years.

This bodes well for further growth in industrial robotics. According to IFR, global annual installations of industrial robots are expected to grow by another 10% to close to 570,000 units in 2022. Thereafter, the industry should maintain a medium to upper single-digit CAGR in 2022-25.

New Types of Metals and Alloys May Be Found with Machine Learning

Researchers from the Max Plank institute have identified 17 possible new metals with useful properties such as resistance to rust, and extreme temperatures using the power of Machine Learning. Scientists typically run experiments to find such metals to find ways to combine metals to create new ones. They start off with one well-known element, like iron, which is cheap and malleable, and add one or two others to see the effect on the original material. It is a laborious process of trial and error that inevitably yields more failures than useful results.

But, using AI, researchers can far more precisely predict which combinations of metals will show promise. The team from the Max Plank institute was hunting for metals with a low level of “invar,” which refers to how much materials expand or contract when exposed to high or low temperatures. Metals with low invar do not change size under extreme temperatures.

This could be useful in a range of sectors, for example, metals that perform well at lower temperatures could improve spacecraft, or boats to ensure they remain resistant to corrosion and rust.

Hundreds of data points representing the characteristics of current metal alloys were used to train the models. This was used by the AI to forecast the appearance of new metals with low invar. The findings of the measurements were then given back into the machine-learning model when those metals were produced in a lab. The researchers tested the suggested metal combinations, fed the results back into the model, and so on until the 17 potential new metals emerged.