The Biggest Paradox in Robotics

The Biggest Paradox in Robotics

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 Bulletin -

  • More domestic robots
  • More Dall-E-2 and why roboticists need to use it as inspiration
  • Robot surgeons - Good or Bad?
  • Tesla’s Gigapress - Bad…
  • Somebody is watching you
  • Design vs evolution - why robots cracked art before grasping

Snippets

DOMEstic Bliss - Last week, we saw BEHAVIOR: a virtual environment for testing robots on household tasks. This week, we bring you the equally-questionably-acronymed DOME (Demonstrate Once, iMitate immEdiately). DOME is a new control methodology that has allowed a robot to reach 100% success rate in 7 household tasks after only a single demonstration. You could say that this is one Dome demo that dominates the domestic domain.

Eric Jang on Generative Modelling - The former Google roboticist and current vice president of AI at Halodi Robotics, runs through how researchers can seek inspiration from the rapid progress of generative modelling. Quite technical, and pairs nicely with this week’s Deep Dive. As a bonus, he’s also used Dall-E-2 to generate bespoke stock images (oxymoron?), which exactly match his arguments.  Say goodbye to clipart, stock photos and a lot more - Dall-E-2 may be one of the most disruptive advances of the decade.

Determining whether technology lives or dies - A new theory suggests that the successes of an innovative technology may be driven by its compatibility with available software and hardware, rather than its superiority over other technologies. The author calls this a “hardware lottery” and suggests that AI researchers should ignore hardware at their peril  - the future’s dominant design will be the one most compatible with current CPU and GPU technology.

Bedside Spanner? - Surgical robots are more accurate and precise than humans, but is their development to the detriment of human surgeons? With each robot designed to perform several different tasks, surgeons aren't learning skills that were required of them in the past. Is this a sign of progress, or are we being stitched up?

Casting Doubt -  This is the 3rd bulletin in a row we’re mentioning Tesla’s Gigapress. Unfortunately, 3rd time has not been the charm.  A huge proportion (60%!) of rear frame metal castings made at Giga Grunheide are being rejected due to quality issues. The factory has become a “money furnace” as Tesla has halted production at its German plant to resolve the issues.

Feel like you’re being watched? - The Enabot Ebo Air smart robot was found to be vulnerable to being hacked, meaning it could be used as a mobile surveillance device. Whilst there’s no evidence this happened, it’s another wake up call. Are we happy having roving video cameras and microphones walking around our homes? On the other hand, they are very cute…

Unicorn extinction - Fourteen tech companies became unicorns in July 2022, the lowest count since August 2020 with nine companies.

The Big Idea

How Robots Became Artists Before Holding a Brush

How is it possible that robots can generate beautiful art but still can’t pick up a random object? Last week, we discussed the control strategies of flexible robotics and the challenges entailed with grasping. In parallel, it's being announced that AI’s are designing ad campaigns. So, why has it taken physical motion so long to ketch-up?

Heinz used AI to design Its ad campaign

5 years ago pundits worried that truck drivers, factory workers and restaurant staff were at risk of losing their jobs to automation. No one predicted that artists and designers might be at risk.  It seems counter-intuitive that these “high level” activities are the first to be mastered by a computer, even Bezos is surprised -

“I think if you went back in time 30 or 40 years and asked roboticists and computer scientists, people working on machine learning at the time which problem would be harder to solve: machine vision, natural language understanding or grasping - I think most people would have predicted that we would solve grasping first” - Jeff Bezos, re:Mars 2019

Turns out Bezos is wrong. Scientists have been predicting this since the dawn of AI.

Jack Pearson

London