Why can’t robots just get a grip?

Why can’t robots just get a grip?

Welcome to the Bulletin by Remix Robotics, where we share a summary of the week's need-to-know robotics and automation news.

In today's email -

  • Why a robot’s reach exceeds its grasp (Big Idea)
  • New machine learning hardware
  • Lots of new investment news
  • Hot takes on Amazon iRobot acquisition
  • A free training course in the UK


MIT’s new machine learning hardware - Engineers working on hardware-based deep learning have developed a method of propelling protons through solids at unprecedented speeds, whilst utilising less energy. Why do they want to do this? Even on the most advanced computers, Deep learning algorithms are relatively slow as the code must constantly load and transfer data. A physical chip could do this much faster. In computer-based neural networks, algorithms mathematically weight connections between neurons. Now, MIT researchers have found a way to do this in a hardware analogue, by developing a programmable resistor which can still pass information millions of times faster than the human brain. This is a stepping stone to a physical Deep Learning chip, which would massively speed up machine learning tasks.

Trying to Top Tesla Tech - Jidu Auto, a joint venture by Baidu and Geely, has unveiled the concept for its first smart vehicle. Featuring LIDAR sensors, full voice control and no door handles, the concept vehicle is designed to be the most intelligent electric vehicle on the market. Baidu’s CEO -laid down the gauntlet and called Tesla out directly.

Robotic Bambi - In a scene familiar to those who have witnessed the first steps of a fawn, an AI-equipped robot dog at the University of California, Berkeley taught itself how to walk in an impressive 60 minutes. The algorithm — named Dreamer — is able to project the probability that a future action will achieve its goal, allowing for a quicker learning process. The researchers have already applied the technique to other applications, including pick and place (see this week's Big Idea).

(Re)-Shoring Up UK Manufacturing - David Nieper (a Derbyshire-based clothing manufacturer) —frustrated by disruptions to global supply chains — is emerging as an early proponent of the re-shoring. The company is putting its money where its mouth is and is investing in local communities, focusing on reskilling to allow British manufacturing to become competitive once again. Whilst many doubt the financial viability of fully UK-based manufacturing, this study highlights that the price of high-quality goods can be sufficient to absorb increased labour costs, with benefits to product quality and delivery.

Free “Introduction to Robotics” workshop for SMEs - Engineers from the Bristol Robotics Laboratory are holding a workshop in September for SMEs across the UK’s South West. It's a great way to develop new skills and we recommend calling into Bristol’s iconic Lakota afterwards — you won’t regret it.

Popping and Locking - Researchers at ETH Zurich have developed multi-stable building blocks that allow the construction of actively controllable shapes. These work by using very small pneumatic actuators to cause the structures to “snap-through” to another shape. This sounds much more complex than it is — if you’ve ever used a snap bracelet, you’re basically there!

The Big Idea

Why can’t Robots just get a grip?

The last few weeks we’ve been tip-toeing around one of the biggest bottlenecks in robotics -  picking up and placing random objects. Last week, we discussed Moravec’s Paradox: how tasks that seem very easy for humans can be incredibly challenging for robotics. Nothing epitomises this more than universal grasping -

Universal grasping is the ability to pick up and manipulate a previously unknown object the first time it's seen, without any mistakes.

Grasping is one of the last steps required to bring robotics into parity with humans. Today, the majority of interest in grasping has been driven by E-commerce: an industry where almost every stage has been automated except picking and packing. This stage is currently very challenging due to the sheer variety of products. For example, Amazon has around 12 million unique product variants. But it’s not just e-commerce: Dyson is interested in universal picking for home robotics, and Google has a spinout company which is targeting pretty much every industry imaginable.

Universal picking is such an important topic that we’re going to spend the next few weeks reviewing its challenges and opportunities -

  • This week we’re setting the scene and understanding the scale of the challenge.
  • Next week we’ll explore the state of the art in robot picking + suggest where the future is trending.

How should universal grasping work?

We’re talking about robot systems, so no surprise universal gripping is tackled by the usual suspects -  a robot, grippers, sensors and a control system. We’ll discuss the dominant technologies for each next week. If we abstract away from specific technological solutions, we can say there are 4 stages to grasping: planning the route, grasping the object, manipulating it through space and successfully placing it in the desired location.  Although simple in principle, in practice an autonomous system must perform numerous complex tasks in each stage. We’ve broken down the main ones in this chart -

The 4 Stages of robot grasping (according to Remix)

Why it often doesn't work

Jack Pearson